OSM report 2022-2023
Marissa Dyck
2024-03-25
The first two chunks of this r markdown file after the r setup allow for plot zooming, but it also means that the html file must be opened in a browser to view the document properly. When it knits in RStudio the preview will appear empty but the html when opened in a browser will have all the info and you can click on each plot to Zoom in on it.
Before you begin
Notes
A few notes about this script.
If you are running this with the 2022-2023 data make sure you download the whole (OSM_2022-2023 GitHub repository)[https://github.com/ACMElabUvic/OSM_2022-2023] from the ACMElabUvic GitHub. This will ensure you have all the files, data, and proper folder structure you will need to run this code and associated analyses.
Also make sure you open RStudio through the R project (OSM_2022-2023.Rproj) this will automatically set your working directory to the correct place (wherever you saved the repository) and ensure you don’t have to change the file paths for some of the data.
Lastly, if you are looking to adapt this code for a future year of data, you will want to ensure you have run the ACME_camera_script_9-2-2024.R or .Rmd with your data as there is much data formatting, cleaning, and restructuring that has to be done before this code will work.
If you have question please email the most recent author, currently
Marissa A. Dyck
Postdoctoral research fellow
University of Victoria
School of Environmental Studies
Email: marissadyck17@gmail.com
Install packages
If you don’t already have the following packages installed, use the code below to install them.
install.packages('tidyverse')
install.packages('ggpubr')
install.packages('corrplot')
install.packages('Hmisc')
install.packages('glmmTMB')
install.packages('MuMIn')
install.packages('TMB', type = 'source')
install.packages('rphylopic')Load libraries
Then load the packages to your library.
library(tidyverse) # data tidying, visualization, and much more; this will load all tidyverse packages, can see complete list using tidyverse_packages()
library(ggpubr) # make modificaions to plot for publication (arrange plots)
library(PerformanceAnalytics) #Used to generate a correlation plot## Loading required package: xts## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
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## # dplyr from breaking base R's lag() function. #
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## legendlibrary(Hmisc) # used to generate histograms for all variables in data frame## Loading required package: lattice## Loading required package: survival## Loading required package: Formula##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:dplyr':
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## format.pval, unitslibrary(glmmTMB) #Constructing GLMMs## Warning in checkMatrixPackageVersion(): Package version inconsistency detected.
## TMB was built with Matrix version 1.4.1
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## Please re-install 'TMB' from source using install.packages('TMB', type = 'source') or ask CRAN for a binary version of 'TMB' matching CRAN's 'Matrix' packagelibrary(MuMIn) # for model selection
library(rphylopic) # add animal silhouettes to graphs## You are using rphylopic v.1.3.0. Please remember to credit PhyloPic contributors (hint: `get_attribution()`) and cite rphylopic in your work (hint: `citation("rphylopic")`).Data
Load detection data
Read in saved and cleaned detection data from the ACME_camera_script_9-2-2024.R.
# detection data
# read in saved and cleaned detection data from the ACME_camera_script_9-2-2024.R
detections <- read_csv('data/processed/OSM_2022_ind_det.csv') %>%
# change site, species and event_id to factor
mutate_if(is.character,
as.factor)## Rows: 14102 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): array, site, species, event_id
## dbl (3): month, year, timediff
## dttm (1): datetime
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Data formatting
In order to get plots that have the same formatting as last years’ report we have to do a bit of data formatting. First we need to make sure we are including the same relevant species (some were ignored for last years’ report or grouped together)
Last years report had the following species
- white-tailed deer
- snowshoe hare
- black bear
- coyote
- red squirrel
- fisher
- unknown
- moose
- lynx
- spruce grouse
- red fox
- striped skunk
- ruffed grouse
- owl
- grey wolf
- domestic dog
- cougar
- raven
- other
- mule deer
And they grouped all humans except for staff as ‘Humans’. Let’s look at the species we have in this year’s data and try to format it the same way
detections %>%
# group by array and species
group_by(array, species) %>%
summarise(n = n()) %>%
# have R print everything
print(n = nrow(.))## `summarise()` has grouped output by 'array'. You can override using the
## `.groups` argument.## # A tibble: 119 × 3
## # Groups: array [4]
## array species n
## <fct> <fct> <int>
## 1 LU01 Beaver 1
## 2 LU01 Black bear 380
## 3 LU01 Cougar 7
## 4 LU01 Coyote 581
## 5 LU01 Domestic dog 6
## 6 LU01 Fisher 111
## 7 LU01 Grey jay 14
## 8 LU01 Grey wolf 21
## 9 LU01 Human 3
## 10 LU01 Lynx 55
## 11 LU01 Moose 99
## 12 LU01 Other 1
## 13 LU01 Other birds 60
## 14 LU01 Otter 2
## 15 LU01 Owl 2
## 16 LU01 Porcupine 5
## 17 LU01 Raven 6
## 18 LU01 Red fox 50
## 19 LU01 Red squirrel 879
## 20 LU01 Ruffed grouse 14
## 21 LU01 Short-tailed weasel 5
## 22 LU01 Snowshoe hare 1443
## 23 LU01 Spruce grouse 12
## 24 LU01 Staff 71
## 25 LU01 Striped skunk 39
## 26 LU01 Unknown 210
## 27 LU01 Unknown canid 48
## 28 LU01 Unknown deer 175
## 29 LU01 Unknown mustelid 13
## 30 LU01 Unknown ungulate 8
## 31 LU01 White-tailed deer 1953
## 32 LU13 ATVer 31
## 33 LU13 Black bear 275
## 34 LU13 Caribou 3
## 35 LU13 Coyote 187
## 36 LU13 Fisher 5
## 37 LU13 Grey jay 2
## 38 LU13 Grey wolf 52
## 39 LU13 Human 2
## 40 LU13 Hunter 1
## 41 LU13 Long-tailed weasel 1
## 42 LU13 Lynx 115
## 43 LU13 Marten 27
## 44 LU13 Moose 128
## 45 LU13 Other birds 12
## 46 LU13 Owl 1
## 47 LU13 Red fox 2
## 48 LU13 Red squirrel 240
## 49 LU13 Ruffed grouse 7
## 50 LU13 Short-tailed weasel 7
## 51 LU13 Snowshoe hare 573
## 52 LU13 Spruce grouse 25
## 53 LU13 Staff 82
## 54 LU13 Striped skunk 1
## 55 LU13 Unknown 86
## 56 LU13 Unknown canid 10
## 57 LU13 Unknown deer 5
## 58 LU13 Unknown mustelid 3
## 59 LU13 White-tailed deer 86
## 60 LU13 Wolverine 8
## 61 LU15 ATVer 1
## 62 LU15 Beaver 2
## 63 LU15 Black bear 220
## 64 LU15 Canada goose 3
## 65 LU15 Caribou 51
## 66 LU15 Coyote 171
## 67 LU15 Fisher 25
## 68 LU15 Grey jay 21
## 69 LU15 Grey wolf 61
## 70 LU15 Long-tailed weasel 15
## 71 LU15 Lynx 122
## 72 LU15 Marten 63
## 73 LU15 Moose 157
## 74 LU15 Other birds 59
## 75 LU15 Otter 5
## 76 LU15 Owl 1
## 77 LU15 Red fox 39
## 78 LU15 Red squirrel 643
## 79 LU15 Ruffed grouse 11
## 80 LU15 Short-tailed weasel 7
## 81 LU15 Snowmobiler 1
## 82 LU15 Snowshoe hare 611
## 83 LU15 Spruce grouse 21
## 84 LU15 Staff 78
## 85 LU15 Unknown 98
## 86 LU15 Unknown canid 7
## 87 LU15 Unknown deer 47
## 88 LU15 Unknown mustelid 16
## 89 LU15 Unknown ungulate 5
## 90 LU15 White-tailed deer 429
## 91 LU21 Black bear 544
## 92 LU21 Canada goose 1
## 93 LU21 Caribou 16
## 94 LU21 Cougar 2
## 95 LU21 Coyote 51
## 96 LU21 Fisher 46
## 97 LU21 Grey jay 13
## 98 LU21 Grey wolf 55
## 99 LU21 Long-tailed weasel 1
## 100 LU21 Lynx 72
## 101 LU21 Marten 50
## 102 LU21 Moose 233
## 103 LU21 Other 1
## 104 LU21 Other birds 44
## 105 LU21 Owl 8
## 106 LU21 Red fox 14
## 107 LU21 Red squirrel 219
## 108 LU21 Ruffed grouse 11
## 109 LU21 Short-tailed weasel 2
## 110 LU21 Snowmobiler 6
## 111 LU21 Snowshoe hare 284
## 112 LU21 Spruce grouse 19
## 113 LU21 Staff 71
## 114 LU21 Unknown 162
## 115 LU21 Unknown canid 5
## 116 LU21 Unknown deer 65
## 117 LU21 Unknown mustelid 23
## 118 LU21 Unknown ungulate 4
## 119 LU21 White-tailed deer 839# now let's create a new data frame (tibble) to work with for the OSM figure summaries specifically
# I personally would lump all the unknown together and all the birds together but for the sake of consistency with last years' figures we will remove some entries, let's create a vector of entries to drop
species_drop <- c('Staff',
'Unknown deer',
'Unknown ungulate',
'Unknown canid',
'Unknown mustelid',
'Other birds')
# now we can create the new data frame with some changes consistent w/ choices made for 2021-2022
detections <- detections %>%
# for summarizing, lets lump all the recreational humans into "Humans"
mutate(species = recode_factor(species,
"Snowmobiler" = "Human",
"ATVer" = "Human",
'Hunter' = 'Human')) %>%
# remove species we don't want to plot
filter(!species %in% species_drop)We will also want to subset the data by landscape unit (LU) and generate a new data frame for each LU to use for plotting
# we will also want to create a data frame for each LU to plot individually
# LU1
dets_LU1 <- detections %>%
filter(array == 'LU01')
# LU13
dets_LU13 <- detections %>%
filter(array == 'LU13')
# LU15
dets_LU15 <- detections %>%
filter(array == 'LU15')
# LU21
dets_LU21 <- detections %>%
filter(array == 'LU21')ANDREW HELP
Can you make the above code into a forloop which assigns each new data frame created from subsetting as dets_LUname?
Detection plots
Detection data
Now we can apply the same data formatting for each LUs’ data frame using purrr.
We want to count the number of independent detections per species per LU to use in the detection plots
# apply the same formatting to each LU data frame using purrr map
detection_data <- list(dets_LU1,
dets_LU13,
dets_LU15,
dets_LU21) %>%
purrr::map(
~.x %>%
# group by species
group_by(species) %>%
# calculate a column with unique accounts of each species
mutate(count = n_distinct(event_id)) %>%
# keep just the columns we need
select(species, count) %>%
# keep only unique (distinct) rows so we should be left with one row per species, this helps with plotting later if you don't do it ggplot will try to count and plot each row it's annoying
distinct()) %>%
# set names of list objects
purrr::set_names('Detections LU01',
'Detections LU13',
'Detections LU15',
'Detections LU21')Detection plots
Now to graph independent detections for each LU using purrr, this avoids a TON of code repetition needed to plot each one individually
We use purrr::imap() instead of
purrr::map() because imap maintains the variable names in
our list (e.g. Detections LU01, Detections LU13, etc.) which we can then
use to title each plot.
Within purrr::imap() we just paste the code we would use
for a single ggplot since all the graphical elements (except the title
which we change with the file name [.y]) are the same
# create object detection plots which uses the detection_data list (w/ all 4 LUs)
detection_plots <- detection_data %>%
# use imap instead of map as it allows us to use .y to paste the list element names as the plot titles later
purrr::imap(
~.x %>%
# now just copy and paste the ggplot code for the detection graphs
ggplot(.,
aes(x = reorder(species, count), y = count)) +
# plot as bar graph using geom_col so we don't have to provide a y aesthetic
geom_col() +
# switch the x and y axis
coord_flip() +
# add the number of detections at the end of each bar
geom_text(aes(label = count),
color = "black",
size = 3,
hjust = -0.3,
vjust = 0.2) +
# label x and y axis with informative titles
labs(x = 'Species',
y = 'Number of Independent (30 min) Detections') +
# add title to plot with LU name the .y will take the name of whatever you named each list element in the detection_data list, so make sure this name is what you want on the ggtitle
ggtitle(.y) +
# set the theme
theme_classic() +
theme(plot.title = element_text(hjust = 0.5)))
# view plots, this will print each in it's own window so you have to scroll back in the plot viewer pane to look at each one
detection_plots## $`Detections LU01`
##
## $`Detections LU13`
##
## $`Detections LU15`
##
## $`Detections LU21`
Save detection plots
Now we want to save these plots in case we need each individual one (we will combine the detection and naive occ plots into a single figure for each LU later and use those for the OSM report, but we may want these standalone plots later so let’s save them while they are here).
We can save all the plots from the purrr iteration above using
purrr::imap. imap is used instead of map because it allows
us to retain the list object names (plot names) to paste as the file
name with the .y command.
IMPORTANT if you are using this code for a future github repo, DO NOT use .tiff as the file extension. This will cause issues when trying to push any changes to the github repo as the files are too large to meet githubs requirements
# save plots only use if needed
purrr::imap(
detection_plots,
~ggsave(.x,
file = paste0("figures/",
.y,
'.jpg'), # avoid using .tiff extension in the github repo, those files are too large to push to origin
dpi = 600,
width = 11,
height = 9,
units = 'in'))## $`Detections LU01`
## [1] "figures/Detections LU01.jpg"
##
## $`Detections LU13`
## [1] "figures/Detections LU13.jpg"
##
## $`Detections LU15`
## [1] "figures/Detections LU15.jpg"
##
## $`Detections LU21`
## [1] "figures/Detections LU21.jpg"Naive occupancy
Data
We also need to alter the detection data a bit to use for naive occupancy plots.
We will use the individual LU detection data like we did before and
use purrr::map() to apply the dame data formatting to all 4
data frames.
Here we want to calculate the total number of sites in each LU, the number of sites each species was detected at in each LU and then use both those numbers to calculate naive occupancy for each species in each LU
# First we need to alter the data frame a bit for these plots, let's create a data frame for each LU (I couldn't figure out how to do this without assigning individual data frames for each UGH)
# apply the same formatting to each data frame using purrr
occupancy_data <- list(dets_LU1,
dets_LU13,
dets_LU15,
dets_LU21) %>%
purrr::map(
~.x %>%
# calculate the total number of sites for each LU
mutate(total_sites = n_distinct(site)) %>%
# group by species to calculate the number of sites each spp occurred at
group_by(species) %>%
# add columns to count the number of sites each spp occurred at and then the naive occupancy
reframe(count = n_distinct(site),
naive_occ = count/total_sites,
ind_det = n_distinct(event_id)) %>%
# keep just the columns we need
select(species, naive_occ, ind_det) %>%
# keep only unique (distinct) rows so we should be left with one row per species, this helps with plotting
distinct()) %>%
purrr::set_names('Naive Occupancy LU01',
'Naive Occupancy LU13',
'Naive Occupancy LU15',
'Naive Occupancy LU21')Occupancy plots
Now we can graph naive occupancy for each LU using purrr, and as with the detection plots this saves a massive amount of coding using purrr to run an iteration on the data files and produce four plots at once instead of copying and pasting code for each individually
# create object occupancy_plots which uses the occupancy_data list (w/ all 4 LUs)
occupancy_plots <- occupancy_data %>%
# use imap instead of map as it allows us to use .y to paste the list element names as the plot titles later
purrr::imap(
~.x %>%
# now just copy and paste the ggplot code for the occupancy graphs
ggplot(.,
aes(x = fct_reorder(species,
ind_det), # this reorders the species so they match the order of the detection plot which makes it better for viewing when the plots are arranged together in 1 figure for each LU
y = naive_occ)) +
# plot as bars using geom_col() which uses stat = 'identity', instead of geom_bar() which will count the rows in each group and plot that instead of naive occ
geom_col() +
# flip x and y axis
coord_flip() +
# add text to end of bars that provides naive occ value
geom_text(aes(label = round(naive_occ, 2)),
size = 3,
hjust = -0.3,
vjust = 0.2) +
# relabel x and y axis and title
labs(x = 'Species',
y = 'Proportion of Sites With At Least One Detection') +
# set plot title using .y (name of list object)
ggtitle(.y) +
# set. theme elements
theme_classic()+
theme(plot.title = element_text(hjust = 0.5)))
# view plots
occupancy_plots## $`Naive Occupancy LU01`
##
## $`Naive Occupancy LU13`
##
## $`Naive Occupancy LU15`
##
## $`Naive Occupancy LU21`
Save occupancy plots
As with the detection plots, we might want these individual plots
later for something so we can use purrr::imap() to save
them to the figures folder
Again avoid using the .tiff extension in github
# save plots
purrr::imap(
occupancy_plots,
~ggsave(.x,
file = paste0("figures/",
.y,
'.jpg'), # avoid using .tiff extension in the github repo, those files are too large to push to origin
dpi = 600,
width = 11,
height = 9,
units = 'in'))## $`Naive Occupancy LU01`
## [1] "figures/Naive Occupancy LU01.jpg"
##
## $`Naive Occupancy LU13`
## [1] "figures/Naive Occupancy LU13.jpg"
##
## $`Naive Occupancy LU15`
## [1] "figures/Naive Occupancy LU15.jpg"
##
## $`Naive Occupancy LU21`
## [1] "figures/Naive Occupancy LU21.jpg"Final combined plots for report
The previous year’s report had a figure for each LU with the
detections plot on the top and the occupancy plot on the bottom so we
will recreate these for this year using ggarrange().
Unfortunately I could not figure out how to do this in purrr to reduce coding but luckily it isn’t too much repitition
# not sure I know how to do the following section in purrr just yet, but we've saved a ton of coding so far and it doesn't take much to arrange each of these individually
# LU1
# arrange the plots so each LU has a figure with detections on top and naive occ on bottom
LU1_det_occ_plots <- ggarrange(detection_plots$`Detections LU01`, occupancy_plots$`Naive Occupancy LU01`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU1_det_occ_plots
# LU13
# arrange the plots so each LU has a figure with detections on top and naive occ on bottom
LU13_det_occ_plots <- ggarrange(detection_plots$`Detections LU13`, occupancy_plots$`Naive Occupancy LU13`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU13_det_occ_plots
# LU15
# arrange the plots so each LU has a figure with detections on top and naive occ on bottom
LU15_det_occ_plots <- ggarrange(detection_plots$`Detections LU15`, occupancy_plots$`Naive Occupancy LU15`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU15_det_occ_plots
# LU21
# arrange the plots so each LU has a figure with detections on top and naive occ on bottom
LU21_det_occ_plots <- ggarrange(detection_plots$`Detections LU21`, occupancy_plots$`Naive Occupancy LU21`,
labels = c("A", "B"),
nrow = 2)
# view plot
LU21_det_occ_plots
We can however, save all the figures again using purrr
# save all figures at once using purrr
final_det_occ_plots <- list(LU1_det_occ_plots,
LU13_det_occ_plots,
LU15_det_occ_plots,
LU21_det_occ_plots) %>%
purrr::set_names('LU01_det_occ_plots',
'LU13_det_occ_plots',
'LU15_det_occ_plots',
'LU21_det_occ_plots') %>%
purrr::imap(
~ggsave(.x,
file = paste0("figures/",
.y,
'.jpg'), # avoid using .tiff extension in the github repo, those files are too large to push to origin
dpi = 600,
width = 12,
height = 15,
units = 'in'))Analysis prep
Read in data
We need the proportional binomial data and the covariate data (from the ACME_camera_script_9-2-2024.R or .Rmd), let’s read those in now and check the structure of each
# response metric (proportional detections from the from the ACME_camera_script_9-2-2024.R or .Rmd)
prop_detections <- read_csv('data/processed/OSM_2022_proportional_detections.csv')## Rows: 152 Columns: 23
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): site
## dbl (22): black_bear, coyote, fisher, moose, white-tailed_deer, cougar, grey...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# check variable structure
str(prop_detections)## spc_tbl_ [152 × 23] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ site : chr [1:152] "LU01_06" "LU01_10" "LU01_11" "LU01_13" ...
## $ black_bear : num [1:152] 7 3 4 7 8 9 4 5 7 7 ...
## $ coyote : num [1:152] 4 4 8 10 11 9 11 0 9 4 ...
## $ fisher : num [1:152] 5 3 3 3 2 1 1 1 0 3 ...
## $ moose : num [1:152] 3 2 5 9 1 0 2 4 1 0 ...
## $ white-tailed_deer : num [1:152] 12 5 12 12 13 14 15 9 12 10 ...
## $ cougar : num [1:152] 0 0 1 0 1 0 0 0 0 0 ...
## $ grey_wolf : num [1:152] 0 0 2 0 0 0 1 0 0 0 ...
## $ lynx : num [1:152] 0 0 1 0 1 1 0 0 0 2 ...
## $ red_fox : num [1:152] 0 0 2 0 0 0 0 0 4 0 ...
## $ wolverine : num [1:152] 0 0 0 0 0 0 0 0 0 0 ...
## $ caribou : num [1:152] 0 0 0 0 0 0 0 0 0 0 ...
## $ absent_black_bear : num [1:152] 5 3 8 5 4 3 8 7 5 5 ...
## $ absent_coyote : num [1:152] 10 1 6 5 3 5 4 15 6 11 ...
## $ absent_fisher : num [1:152] 9 2 11 12 12 13 14 14 15 12 ...
## $ absent_moose : num [1:152] 11 3 9 6 13 14 13 11 14 15 ...
## $ absent_white-tailed_deer: num [1:152] 2 0 2 3 1 0 0 6 3 5 ...
## $ absent_cougar : num [1:152] 14 5 13 15 13 14 15 15 15 15 ...
## $ absent_grey_wolf : num [1:152] 14 5 12 15 14 14 14 15 15 15 ...
## $ absent_lynx : num [1:152] 14 5 13 15 13 13 15 15 15 13 ...
## $ absent_red_fox : num [1:152] 14 5 12 15 14 14 15 15 11 15 ...
## $ absent_wolverine : num [1:152] 14 5 14 15 14 14 15 15 15 15 ...
## $ absent_caribou : num [1:152] 14 5 14 15 14 14 15 15 15 15 ...
## - attr(*, "spec")=
## .. cols(
## .. site = col_character(),
## .. black_bear = col_double(),
## .. coyote = col_double(),
## .. fisher = col_double(),
## .. moose = col_double(),
## .. `white-tailed_deer` = col_double(),
## .. cougar = col_double(),
## .. grey_wolf = col_double(),
## .. lynx = col_double(),
## .. red_fox = col_double(),
## .. wolverine = col_double(),
## .. caribou = col_double(),
## .. absent_black_bear = col_double(),
## .. absent_coyote = col_double(),
## .. absent_fisher = col_double(),
## .. absent_moose = col_double(),
## .. `absent_white-tailed_deer` = col_double(),
## .. absent_cougar = col_double(),
## .. absent_grey_wolf = col_double(),
## .. absent_lynx = col_double(),
## .. absent_red_fox = col_double(),
## .. absent_wolverine = col_double(),
## .. absent_caribou = col_double()
## .. )
## - attr(*, "problems")=<externalptr># model covariates (merged HFI and VEG data from the ACME_camera_script_9-2-2024.R or .Rmd)
covariates <- read_csv('data/processed/OSM_2022_covariates.csv',
# set the column types to read in correctly
col_types = cols(array = col_factor(),
camera = col_factor(),
site = col_factor(),
buff_dist = col_factor(),
.default = col_number()))
# check variable structure
str(covariates)## spc_tbl_ [3,100 × 77] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ array : Factor w/ 4 levels "LU13","LU15",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ camera : Factor w/ 96 levels "18","15","03",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ site : Factor w/ 155 levels "LU13_18","LU13_15",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ buff_dist : Factor w/ 20 levels "250","500","750",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ harvest_area : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ crop : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ well_aband : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ well_oil : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ trail : num [1:3100] 0 0 NA 0.5 0 ...
## $ harvest_area_white_zone : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ conventional_seismic : num [1:3100] 0.5 0.5 NA 0.5 1 ...
## $ pipeline : num [1:3100] 0 0.5 NA 0 0 0 0.5 0 0 0 ...
## $ tame_pasture : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ rough_pasture : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ rural_residence : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ transmission_line : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ well_gas : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ misc_oil_gas_facility : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ clearing_unknown : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ vegetated_edge_roads : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ road_unimproved : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ road_gravel_1l : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ road_gravel_2l : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ truck_trail : num [1:3100] 0.5 0 NA 0 0 0 0 0 0 0 ...
## $ borrowpits : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ sump : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ borrowpit_wet : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ cultivation_abandoned : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ urban_residence : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ country_residence : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ recreation : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ well_other : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ well_bitumen : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ well_cased : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ road_paved_undiv_2l : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ road_unclassified : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ runway : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ clearing_wellpad_unconfirmed: num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ facility_unknown : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ borrowpit_dry : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ grvl_sand_pit : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ dugout : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ lagoon : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ open_pit_mine : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ low_impact_seismic : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ surrounding_veg : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ transfer_station : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ facility_other : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ vegetated_edge_railways : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ fruit_vegetables : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ residence_clearing : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ cfo : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ landfill : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ well_cleared_not_confirmed : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ oil_gas_plant : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ urban_industrial : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ road_paved_1l : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ road_paved_undiv_1l : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ road_winter : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ well_cleared_not_drilled : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ well_unknown : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ airp_runway : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ reservoir : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ campground : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ canal : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ camp_industrial : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ rlwy_sgl_track : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ lc_class20 : num [1:3100] 0.143 0 0 0 0 ...
## $ lc_class32 : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ lc_class33 : num [1:3100] 0 0.2 0 0 0 ...
## $ lc_class34 : num [1:3100] 0 0.2 0 0 0 ...
## $ lc_class50 : num [1:3100] 0.286 0 0.333 0 0 ...
## $ lc_class110 : num [1:3100] 0.143 0.2 0 0 0.333 ...
## $ lc_class120 : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ lc_class210 : num [1:3100] 0.429 0.2 0.333 1 0.667 ...
## $ lc_class220 : num [1:3100] 0 0 0 0 0 0.25 0 0 0 0 ...
## $ lc_class230 : num [1:3100] 0 0.2 0.333 0 0 ...
## - attr(*, "spec")=
## .. cols(
## .. .default = col_number(),
## .. array = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
## .. camera = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
## .. site = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
## .. buff_dist = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
## .. harvest_area = col_number(),
## .. crop = col_number(),
## .. well_aband = col_number(),
## .. well_oil = col_number(),
## .. trail = col_number(),
## .. harvest_area_white_zone = col_number(),
## .. conventional_seismic = col_number(),
## .. pipeline = col_number(),
## .. tame_pasture = col_number(),
## .. rough_pasture = col_number(),
## .. rural_residence = col_number(),
## .. transmission_line = col_number(),
## .. well_gas = col_number(),
## .. misc_oil_gas_facility = col_number(),
## .. clearing_unknown = col_number(),
## .. vegetated_edge_roads = col_number(),
## .. road_unimproved = col_number(),
## .. road_gravel_1l = col_number(),
## .. road_gravel_2l = col_number(),
## .. truck_trail = col_number(),
## .. borrowpits = col_number(),
## .. sump = col_number(),
## .. borrowpit_wet = col_number(),
## .. cultivation_abandoned = col_number(),
## .. urban_residence = col_number(),
## .. country_residence = col_number(),
## .. recreation = col_number(),
## .. well_other = col_number(),
## .. well_bitumen = col_number(),
## .. well_cased = col_number(),
## .. road_paved_undiv_2l = col_number(),
## .. road_unclassified = col_number(),
## .. runway = col_number(),
## .. clearing_wellpad_unconfirmed = col_number(),
## .. facility_unknown = col_number(),
## .. borrowpit_dry = col_number(),
## .. grvl_sand_pit = col_number(),
## .. dugout = col_number(),
## .. lagoon = col_number(),
## .. open_pit_mine = col_number(),
## .. low_impact_seismic = col_number(),
## .. surrounding_veg = col_number(),
## .. transfer_station = col_number(),
## .. facility_other = col_number(),
## .. vegetated_edge_railways = col_number(),
## .. fruit_vegetables = col_number(),
## .. residence_clearing = col_number(),
## .. cfo = col_number(),
## .. landfill = col_number(),
## .. well_cleared_not_confirmed = col_number(),
## .. oil_gas_plant = col_number(),
## .. urban_industrial = col_number(),
## .. road_paved_1l = col_number(),
## .. road_paved_undiv_1l = col_number(),
## .. road_winter = col_number(),
## .. well_cleared_not_drilled = col_number(),
## .. well_unknown = col_number(),
## .. airp_runway = col_number(),
## .. reservoir = col_number(),
## .. campground = col_number(),
## .. canal = col_number(),
## .. camp_industrial = col_number(),
## .. rlwy_sgl_track = col_number(),
## .. lc_class20 = col_number(),
## .. lc_class32 = col_number(),
## .. lc_class33 = col_number(),
## .. lc_class34 = col_number(),
## .. lc_class50 = col_number(),
## .. lc_class110 = col_number(),
## .. lc_class120 = col_number(),
## .. lc_class210 = col_number(),
## .. lc_class220 = col_number(),
## .. lc_class230 = col_number()
## .. )
## - attr(*, "problems")=<externalptr>Format covariates
There are too many covariates to include in the models individually and many of them describe similar HFI features. We can use the info from the README file in this repository which includes detailed descriptions from the ABMI human footprints wall to wall data download website for Year 2021 OR in the relevant_literature folder of this repository (HFI_2021_v1_0_Metadata_Final.pdf).
the current version of this code for the purposes of the 2022-2023 report used a merged dataset from 2021-2022 and 2022-2023 data, howver each year of data the variables were extracted slightly differenty from GIS so final version of this code will include a different formatting process which will likely occur in the ACME_camera_script_9-2-2024.R or .Rmd
covariates_grouped <- covariates %>%
mutate(borrowpits = rowSums(across(contains('borrowpit'))),
industrial_sites = camp_industrial + oil_gas_plant + open_pit_mine +
rowSums(across(contains('facility'))),
seismic_lines = rowSums(across(contains('seismic'))),
wellsites = rowSums(across(contains('well'))),
roads = rowSums(across(contains('road'))),
havest_areas = rowSums(across(contains('harvest'))),
trails = rowSums(across(contains('trail'))),
residences = rowSums(across(contains('residence'))),
pasture = rowSums(across(contains('pasture'))),
other_transportation_features = runway + airp_runway + rlwy_sgl_track + vegetated_edge_railways,
crops = crop + fruit_vegetables + cultivation_abandoned,
water = lagoon + reservoir + dugout + canal,
.keep = 'unused') %>%
# remove features we don't need
select(!c(recreation,
clearing_unknown,
cfo,
grvl_sand_pit,
transfer_station,
campground,
surrounding_veg,
urban_industrial,
landfill,
sump,
water,
crops,
other_transportation_features,
pasture,
residences
)) %>%
# reorder variables
relocate(c(pipeline,
transmission_line,
borrowpits),
.after = lc_class230)
# see what's left
names(covariates_grouped)## [1] "array" "camera" "site"
## [4] "buff_dist" "lc_class20" "lc_class32"
## [7] "lc_class33" "lc_class34" "lc_class50"
## [10] "lc_class110" "lc_class120" "lc_class210"
## [13] "lc_class220" "lc_class230" "pipeline"
## [16] "transmission_line" "borrowpits" "industrial_sites"
## [19] "seismic_lines" "wellsites" "roads"
## [22] "havest_areas" "trails"# check the structure of new data
str(covariates_grouped)## tibble [3,100 × 23] (S3: tbl_df/tbl/data.frame)
## $ array : Factor w/ 4 levels "LU13","LU15",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ camera : Factor w/ 96 levels "18","15","03",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ site : Factor w/ 155 levels "LU13_18","LU13_15",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ buff_dist : Factor w/ 20 levels "250","500","750",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ lc_class20 : num [1:3100] 0.143 0 0 0 0 ...
## $ lc_class32 : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ lc_class33 : num [1:3100] 0 0.2 0 0 0 ...
## $ lc_class34 : num [1:3100] 0 0.2 0 0 0 ...
## $ lc_class50 : num [1:3100] 0.286 0 0.333 0 0 ...
## $ lc_class110 : num [1:3100] 0.143 0.2 0 0 0.333 ...
## $ lc_class120 : num [1:3100] 0 0 0 0 0 0 0 0 0 0 ...
## $ lc_class210 : num [1:3100] 0.429 0.2 0.333 1 0.667 ...
## $ lc_class220 : num [1:3100] 0 0 0 0 0 0.25 0 0 0 0 ...
## $ lc_class230 : num [1:3100] 0 0.2 0.333 0 0 ...
## $ pipeline : num [1:3100] 0 0.5 NA 0 0 0 0.5 0 0 0 ...
## $ transmission_line: num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ borrowpits : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ industrial_sites : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ seismic_lines : num [1:3100] 0.5 0.5 NA 0.5 1 ...
## $ wellsites : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ roads : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ havest_areas : num [1:3100] 0 0 NA 0 0 0 0 0 0 0 ...
## $ trails : num [1:3100] 0.5 0 NA 0.5 0 ...# check summary of new data
summary(covariates_grouped)## array camera site buff_dist lc_class20
## LU13:820 27 : 80 LU13_18: 20 250 : 155 Min. :0.00000
## LU15:780 32 : 80 LU13_15: 20 500 : 155 1st Qu.:0.00000
## LU21:720 41 : 80 LU13_03: 20 750 : 155 Median :0.00000
## LU01:780 36 : 80 LU13_34: 20 1000 : 155 Mean :0.02914
## 16 : 60 LU13_57: 20 1250 : 155 3rd Qu.:0.04545
## 21 : 60 LU13_16: 20 1500 : 155 Max. :0.37500
## (Other):2660 (Other):2980 (Other):2170
## lc_class32 lc_class33 lc_class34 lc_class50
## Min. :0.000e+00 Min. :0.000000 Min. :0.000000 Min. :0.0000
## 1st Qu.:0.000e+00 1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.1535
## Median :0.000e+00 Median :0.000000 Median :0.004525 Median :0.2500
## Mean :4.373e-05 Mean :0.012162 Mean :0.024762 Mean :0.2733
## 3rd Qu.:0.000e+00 3rd Qu.:0.009346 3rd Qu.:0.021739 3rd Qu.:0.3750
## Max. :2.273e-02 Max. :0.400000 Max. :0.500000 Max. :0.9091
##
## lc_class110 lc_class120 lc_class210 lc_class220
## Min. :0.00000 Min. :0.000000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.04918 1st Qu.:0.000000 1st Qu.:0.1270 1st Qu.:0.05882
## Median :0.13333 Median :0.000000 Median :0.1901 Median :0.17903
## Mean :0.13762 Mean :0.001355 Mean :0.2120 Mean :0.16350
## 3rd Qu.:0.20000 3rd Qu.:0.000000 3rd Qu.:0.2568 3rd Qu.:0.25000
## Max. :0.66667 Max. :0.200000 Max. :1.0000 Max. :0.66667
##
## lc_class230 pipeline transmission_line borrowpits
## Min. :0.00000 Min. :0.00000 Min. :0.000000 Min. :0.00000
## 1st Qu.:0.06667 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.00000
## Median :0.13208 Median :0.03637 Median :0.000000 Median :0.00000
## Mean :0.14614 Mean :0.06940 Mean :0.004712 Mean :0.01108
## 3rd Qu.:0.21053 3rd Qu.:0.10638 3rd Qu.:0.000000 3rd Qu.:0.01807
## Max. :0.66667 Max. :1.00000 Max. :0.500000 Max. :0.16667
## NA's :8 NA's :8 NA's :8
## industrial_sites seismic_lines wellsites roads
## Min. :0.000000 Min. :0.0000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.000000 1st Qu.:0.2774 1st Qu.:0.01541 1st Qu.:0.00000
## Median :0.000000 Median :0.3868 Median :0.04408 Median :0.05939
## Mean :0.001448 Mean :0.4173 Mean :0.05748 Mean :0.15189
## 3rd Qu.:0.000000 3rd Qu.:0.5000 3rd Qu.:0.08125 3rd Qu.:0.27978
## Max. :0.111111 Max. :1.0000 Max. :0.50000 Max. :0.83333
## NA's :8 NA's :8 NA's :8 NA's :8
## havest_areas trails
## Min. :0.00000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.0617
## Median :0.00000 Median :0.1522
## Mean :0.04801 Mean :0.1874
## 3rd Qu.:0.04506 3rd Qu.:0.2712
## Max. :0.83333 Max. :1.0000
## NA's :8 NA's :8# there are some NAs in the data which will cause problems with modeling/visualization of data ignore for now but will explore these sites specifically after report
covariates_grouped <- covariates_grouped %>%
# remove rows with NAs
na.omit()Subset data & correlation plots
Marissa try to get the purrr code for this to work later
Now we need to subset the data for each buffer width, and then in the same loop let’s make correlation plots for these variables within each buffer
# Couldn't get this to work in purrr yet so using a loop to subset the data, create the plots, and save them all in one section... NEAT
buffer_frames<-list()
for (i in unique(covariates_grouped$buff_dist)){
print(i)
#Subset data based on radius
df<-covariates_grouped%>%
filter(buff_dist == i)
#rename dataframe on the fly
assign(paste("df", i, sep ="_"), df)
#list of dataframes
buffer_frames<-c(buffer_frames, list(df))
#Subset data based on radius
df<-covariates_grouped%>%
filter(buff_dist == i)%>%
select(where(is.numeric))
#compute a correlation matrix (watch for errors)
matrix<-cor(df)
#print and save the correlation plot on the go
#renaming for each buffer as we do
png(file.path("figures/", paste("correlation_", i, ".png")))
corrplot::corrplot(matrix,
type = 'upper',
tl.col = 'black',
title = paste0('Variable correlation plot at ', i))
dev.off()
}## [1] "250"## Warning in cor(df): the standard deviation is zero## [1] "500"## Warning in cor(df): the standard deviation is zero## [1] "750"## Warning in cor(df): the standard deviation is zero## [1] "1000"## Warning in cor(df): the standard deviation is zero## [1] "1250"## Warning in cor(df): the standard deviation is zero## [1] "1500"## Warning in cor(df): the standard deviation is zero## [1] "1750"## Warning in cor(df): the standard deviation is zero## [1] "2000"## Warning in cor(df): the standard deviation is zero## [1] "2250"## Warning in cor(df): the standard deviation is zero## [1] "2500"## Warning in cor(df): the standard deviation is zero## [1] "2750"## Warning in cor(df): the standard deviation is zero## [1] "3000"## Warning in cor(df): the standard deviation is zero## [1] "3250"## Warning in cor(df): the standard deviation is zero## [1] "3500"## Warning in cor(df): the standard deviation is zero## [1] "3750"## [1] "4000"## [1] "4250"## [1] "4500"## [1] "4750"## [1] "5000"# name list objects so we can extract names for plotting
buffer_frames <- buffer_frames %>%
# absurdly long way to do this but for sake of time fuck it
purrr::set_names('250 meter buffer',
'500 meter buffer',
'750 meter buffer',
'1000 meter buffer',
'1250 meter buffer',
'1500 meter buffer',
'1750 meter buffer',
'2000 meter buffer',
'2250 meter buffer',
'2500 meter buffer',
'2750 meter buffer',
'3000 meter buffer',
'3250 meter buffer',
'3500 meter buffer',
'3750 meter buffer',
'4000 meter buffer',
'4250 meter buffer',
'4500 meter buffer',
'4750 meter buffer',
'5000 meter buffer')Exploratory plots
add more to this section in later when we have more time to explore the covariates and choose which should be inlcuded etc.
hfi_histograms <- buffer_frames %>%
purrr::imap(
~.x %>%
# filter to just the HFI variables
select(where(is.numeric) &
! starts_with('lc_class')) %>%
# pipe into hist.data.frame function to make histograms for each variable
hist.data.frame(mtitl = paste0('Histograms of HFI variables at ', .y)))
Now let’s do the same thing with the landcover variables
lc_histograms <- buffer_frames %>%
purrr::imap(
~.x %>%
# filter to just the landcover variables
select(where(is.numeric) &
starts_with('lc_class')) %>%
# pipe into hist.data.frame function to make histograms for each variable
hist.data.frame(mtitl = paste0('Histograms of landcover variables at ', .y)))
Add response metric
Now that we have the covariate data formatted we need to add the response metric (monthly proportional presence/absence) to the data frames
final_df <- buffer_frames %>%
purrr::map(
~.x %>%
left_join(prop_detections,
by = 'site'))Analysis
Now we are going to run a global model which includes all HFI and LC variables that at first glance (will do a more thorough check later) seem to have enough data to include as covariates for each buffer width, and then we will compare these models see which buffer width best fit the data for each species.
We don’t need to do ALL the species since many don’t have enough data.
Refer to the ACME_camera_script_9-2-2024.html or .Rmd the plot for proportional monthly detections should provide info on which species we have enough data for, can be found under Response metrics/3.Proportion monthly detections
A brief look at this fig indicates that we have enough for all the mammals in the prop_detections data frame except
- cougar
- wolverine
- caribou??? (may have enough may not)
Black bear
ANDREW/MARISSA FIX later
there is probably a way to shorten the following code to select particular species, I saw Andrew’s for loop in the draft script he wrote but couldn’t quite figure it out so I did this instead, maybe we can merge approaches?
Global model
Let’s start with bears and use purrr to create a global model for every buffer distance
black_bear_mods <- final_df %>%
# use purrr map to fun the same functions for all buffer sizes ((all objects in list))
purrr::map(
~.x %>%
# glmmTMB function let's us run the proportional binomial model using cbind to combine the present and absent columns for each species
glmmTMB::glmmTMB(cbind(black_bear, absent_black_bear) ~
seismic_lines +
pipeline +
borrowpits +
wellsites +
roads +
trails +
lc_class20 +
lc_class34 +
lc_class50 +
lc_class110 +
lc_class210 +
lc_class220 +
lc_class230 +
(1|array),
data = .,
family = 'binomial'))Model selection
We will use the model.sel() function from the
MuMIn package to compare the global models for each buffer
width and see which buffer fits the bear data best
model.sel(black_bear_mods)## Warning in model.sel.default(black_bear_mods): models are not all fitted to the
## same data## Model selection table
## cnd((Int)) dsp((Int)) cnd(brr) cnd(lc_c11) cnd(lc_c20)
## 250 meter buffer -0.15620 + 0.6632 0.1236 -2.907000
## 750 meter buffer -1.95600 + 3.2370 -0.3414 1.344000
## 4250 meter buffer 3.99200 + -8.6930 -6.5210 -3.613000
## 4500 meter buffer 3.02400 + -7.7200 -5.4380 -3.311000
## 500 meter buffer -0.37050 + -2.7320 -0.6889 -0.670300
## 4000 meter buffer 3.41900 + -5.5320 -5.3720 -3.507000
## 1000 meter buffer -0.72430 + 3.9400 -1.4870 -0.003855
## 4750 meter buffer 3.99500 + -4.9810 -5.4840 -3.628000
## 3500 meter buffer 4.61900 + -2.5720 -6.4170 -5.508000
## 1250 meter buffer -0.05382 + -2.4740 -1.9990 -0.202500
## 5000 meter buffer 4.33400 + -3.2840 -5.4490 -5.221000
## 3750 meter buffer 4.15700 + -1.9330 -5.7150 -5.938000
## 2500 meter buffer 3.60400 + 4.0000 -4.3810 -2.807000
## 2000 meter buffer 3.34500 + 2.8120 -5.0190 -2.022000
## 2750 meter buffer 3.32100 + 0.6033 -4.7360 -3.172000
## 1500 meter buffer 2.42300 + 0.1242 -4.4740 -2.707000
## 2250 meter buffer 2.80700 + 4.1350 -4.2460 -2.002000
## 3250 meter buffer 3.42500 + -2.4720 -5.1480 -3.925000
## 3000 meter buffer 2.95400 + -2.5420 -4.9970 -3.353000
## 1750 meter buffer 3.07800 + 2.5640 -4.6540 -1.829000
## cnd(lc_c21) cnd(lc_c22) cnd(lc_c23) cnd(lc_c34) cnd(lc_c50)
## 250 meter buffer -0.8100 1.2680 -0.9658 -0.5266 -0.01751
## 750 meter buffer 1.0660 2.4280 1.5200 0.7237 0.72120
## 4250 meter buffer -4.2940 -5.5240 -5.6240 -32.9500 -5.98100
## 4500 meter buffer -2.8150 -4.7320 -4.7080 -37.3700 -4.95100
## 500 meter buffer -0.2043 1.2970 -0.1915 -0.6064 -0.49080
## 4000 meter buffer -3.4470 -4.8610 -4.7940 -30.8300 -5.16900
## 1000 meter buffer -0.5946 0.5603 -0.7501 0.1574 -0.69290
## 4750 meter buffer -3.7660 -4.7590 -5.2050 -44.9600 -5.66400
## 3500 meter buffer -5.3440 -5.5890 -5.4340 -22.3400 -5.51500
## 1250 meter buffer -1.6130 -0.1985 -1.7500 -0.6162 -1.33100
## 5000 meter buffer -4.3580 -4.6980 -5.6180 -50.0300 -5.91900
## 3750 meter buffer -4.8560 -5.3170 -4.6560 -24.3400 -5.12500
## 2500 meter buffer -3.4410 -4.7590 -4.3690 -19.7200 -4.84600
## 2000 meter buffer -3.6160 -4.4010 -4.1740 -11.3800 -4.56800
## 2750 meter buffer -3.5310 -4.1790 -3.4030 -18.8100 -3.99400
## 1500 meter buffer -3.6120 -3.1860 -3.7450 -5.4970 -3.89000
## 2250 meter buffer -2.6530 -4.1160 -3.7100 -14.4200 -4.07700
## 3250 meter buffer -4.0780 -4.2550 -4.2520 -17.3300 -4.34500
## 3000 meter buffer -3.6690 -3.7370 -3.8960 -13.8300 -3.79600
## 1750 meter buffer -4.1040 -3.6840 -4.0900 -8.0540 -4.40300
## cnd(ppl) cnd(rds) cnd(ssm_lns) cnd(trl) cnd(wll) df logLik
## 250 meter buffer -0.31540 -0.15940 -0.2193 0.12830 -1.1400 15 -281.417
## 750 meter buffer 0.25910 0.22470 0.2466 0.54550 -0.4503 15 -313.123
## 4250 meter buffer 0.19010 1.32400 1.6020 1.50300 -0.3255 15 -314.209
## 4500 meter buffer 0.54310 1.22800 1.6070 1.48000 -0.9885 15 -315.213
## 500 meter buffer -0.40100 -0.13060 -0.1239 -0.10060 0.4296 15 -315.684
## 4000 meter buffer -0.06547 0.87580 1.2940 1.32900 -0.6553 15 -315.865
## 1000 meter buffer 0.37460 0.75610 0.7682 0.81240 -1.2510 15 -315.937
## 4750 meter buffer -0.50120 0.45590 0.9784 0.71950 0.1911 15 -316.072
## 3500 meter buffer -1.33600 0.18180 0.7121 0.40060 2.2800 15 -316.302
## 1250 meter buffer 0.90570 1.08600 0.8048 1.25700 -1.4600 15 -316.465
## 5000 meter buffer -0.35550 0.28690 0.8817 0.80080 -0.5400 15 -316.505
## 3750 meter buffer -0.88800 0.14020 0.6929 0.66420 1.2280 15 -316.593
## 2500 meter buffer -0.70050 -0.10240 0.3952 0.34430 0.9719 15 -316.754
## 2000 meter buffer -0.31350 0.13820 0.5285 0.32790 0.5836 15 -316.992
## 2750 meter buffer -0.55870 -0.39300 0.1135 0.07635 1.9580 15 -317.110
## 1500 meter buffer 0.28900 0.71010 0.8328 1.12900 -0.5994 15 -317.112
## 2250 meter buffer -0.40770 0.09159 0.5077 0.49230 0.7172 15 -317.132
## 3250 meter buffer -1.44300 0.10820 0.6210 0.47640 2.1430 15 -317.557
## 3000 meter buffer -1.10100 0.19610 0.6168 0.44360 2.1800 15 -317.912
## 1750 meter buffer -0.33480 0.27400 0.6091 0.44150 0.5543 15 -318.429
## AICc delta weight
## 250 meter buffer 596.6 0.00 1
## 750 meter buffer 659.8 63.19 0
## 4250 meter buffer 661.9 65.36 0
## 4500 meter buffer 664.0 67.37 0
## 500 meter buffer 664.9 68.31 0
## 4000 meter buffer 665.3 68.67 0
## 1000 meter buffer 665.4 68.82 0
## 4750 meter buffer 665.7 69.09 0
## 3500 meter buffer 666.1 69.55 0
## 1250 meter buffer 666.5 69.88 0
## 5000 meter buffer 666.5 69.95 0
## 3750 meter buffer 666.7 70.13 0
## 2500 meter buffer 667.0 70.45 0
## 2000 meter buffer 667.5 70.93 0
## 2750 meter buffer 667.7 71.16 0
## 1500 meter buffer 667.8 71.17 0
## 2250 meter buffer 667.8 71.21 0
## 3250 meter buffer 668.6 72.06 0
## 3000 meter buffer 669.4 72.77 0
## 1750 meter buffer 670.4 73.80 0
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)hmmmm seems fishy to me that the 250 meter buffer which is the only one that had missing data would perform THAT much better than all the others, and really you shouldn’t compare models if they aren’t run on the same data, hence the warning message
Let’s remove the 250 buffer and see what happens
black_bear_mods_no250 <- black_bear_mods %>%
# purrr::discard_at will remove an item from a list
purrr::discard_at('250 meter buffer')
# run model selection again
model.sel(black_bear_mods_no250)## Model selection table
## cnd((Int)) dsp((Int)) cnd(brr) cnd(lc_c11) cnd(lc_c20)
## 750 meter buffer -1.95600 + 3.2370 -0.3414 1.344000
## 4250 meter buffer 3.99200 + -8.6930 -6.5210 -3.613000
## 4500 meter buffer 3.02400 + -7.7200 -5.4380 -3.311000
## 500 meter buffer -0.37050 + -2.7320 -0.6889 -0.670300
## 4000 meter buffer 3.41900 + -5.5320 -5.3720 -3.507000
## 1000 meter buffer -0.72430 + 3.9400 -1.4870 -0.003855
## 4750 meter buffer 3.99500 + -4.9810 -5.4840 -3.628000
## 3500 meter buffer 4.61900 + -2.5720 -6.4170 -5.508000
## 1250 meter buffer -0.05382 + -2.4740 -1.9990 -0.202500
## 5000 meter buffer 4.33400 + -3.2840 -5.4490 -5.221000
## 3750 meter buffer 4.15700 + -1.9330 -5.7150 -5.938000
## 2500 meter buffer 3.60400 + 4.0000 -4.3810 -2.807000
## 2000 meter buffer 3.34500 + 2.8120 -5.0190 -2.022000
## 2750 meter buffer 3.32100 + 0.6033 -4.7360 -3.172000
## 1500 meter buffer 2.42300 + 0.1242 -4.4740 -2.707000
## 2250 meter buffer 2.80700 + 4.1350 -4.2460 -2.002000
## 3250 meter buffer 3.42500 + -2.4720 -5.1480 -3.925000
## 3000 meter buffer 2.95400 + -2.5420 -4.9970 -3.353000
## 1750 meter buffer 3.07800 + 2.5640 -4.6540 -1.829000
## cnd(lc_c21) cnd(lc_c22) cnd(lc_c23) cnd(lc_c34) cnd(lc_c50)
## 750 meter buffer 1.0660 2.4280 1.5200 0.7237 0.7212
## 4250 meter buffer -4.2940 -5.5240 -5.6240 -32.9500 -5.9810
## 4500 meter buffer -2.8150 -4.7320 -4.7080 -37.3700 -4.9510
## 500 meter buffer -0.2043 1.2970 -0.1915 -0.6064 -0.4908
## 4000 meter buffer -3.4470 -4.8610 -4.7940 -30.8300 -5.1690
## 1000 meter buffer -0.5946 0.5603 -0.7501 0.1574 -0.6929
## 4750 meter buffer -3.7660 -4.7590 -5.2050 -44.9600 -5.6640
## 3500 meter buffer -5.3440 -5.5890 -5.4340 -22.3400 -5.5150
## 1250 meter buffer -1.6130 -0.1985 -1.7500 -0.6162 -1.3310
## 5000 meter buffer -4.3580 -4.6980 -5.6180 -50.0300 -5.9190
## 3750 meter buffer -4.8560 -5.3170 -4.6560 -24.3400 -5.1250
## 2500 meter buffer -3.4410 -4.7590 -4.3690 -19.7200 -4.8460
## 2000 meter buffer -3.6160 -4.4010 -4.1740 -11.3800 -4.5680
## 2750 meter buffer -3.5310 -4.1790 -3.4030 -18.8100 -3.9940
## 1500 meter buffer -3.6120 -3.1860 -3.7450 -5.4970 -3.8900
## 2250 meter buffer -2.6530 -4.1160 -3.7100 -14.4200 -4.0770
## 3250 meter buffer -4.0780 -4.2550 -4.2520 -17.3300 -4.3450
## 3000 meter buffer -3.6690 -3.7370 -3.8960 -13.8300 -3.7960
## 1750 meter buffer -4.1040 -3.6840 -4.0900 -8.0540 -4.4030
## cnd(ppl) cnd(rds) cnd(ssm_lns) cnd(trl) cnd(wll) df logLik
## 750 meter buffer 0.25910 0.22470 0.2466 0.54550 -0.4503 15 -313.123
## 4250 meter buffer 0.19010 1.32400 1.6020 1.50300 -0.3255 15 -314.209
## 4500 meter buffer 0.54310 1.22800 1.6070 1.48000 -0.9885 15 -315.213
## 500 meter buffer -0.40100 -0.13060 -0.1239 -0.10060 0.4296 15 -315.684
## 4000 meter buffer -0.06547 0.87580 1.2940 1.32900 -0.6553 15 -315.865
## 1000 meter buffer 0.37460 0.75610 0.7682 0.81240 -1.2510 15 -315.937
## 4750 meter buffer -0.50120 0.45590 0.9784 0.71950 0.1911 15 -316.072
## 3500 meter buffer -1.33600 0.18180 0.7121 0.40060 2.2800 15 -316.302
## 1250 meter buffer 0.90570 1.08600 0.8048 1.25700 -1.4600 15 -316.465
## 5000 meter buffer -0.35550 0.28690 0.8817 0.80080 -0.5400 15 -316.505
## 3750 meter buffer -0.88800 0.14020 0.6929 0.66420 1.2280 15 -316.593
## 2500 meter buffer -0.70050 -0.10240 0.3952 0.34430 0.9719 15 -316.754
## 2000 meter buffer -0.31350 0.13820 0.5285 0.32790 0.5836 15 -316.992
## 2750 meter buffer -0.55870 -0.39300 0.1135 0.07635 1.9580 15 -317.110
## 1500 meter buffer 0.28900 0.71010 0.8328 1.12900 -0.5994 15 -317.112
## 2250 meter buffer -0.40770 0.09159 0.5077 0.49230 0.7172 15 -317.132
## 3250 meter buffer -1.44300 0.10820 0.6210 0.47640 2.1430 15 -317.557
## 3000 meter buffer -1.10100 0.19610 0.6168 0.44360 2.1800 15 -317.912
## 1750 meter buffer -0.33480 0.27400 0.6091 0.44150 0.5543 15 -318.429
## AICc delta weight
## 750 meter buffer 659.8 0.00 0.504
## 4250 meter buffer 661.9 2.17 0.170
## 4500 meter buffer 664.0 4.18 0.062
## 500 meter buffer 664.9 5.12 0.039
## 4000 meter buffer 665.3 5.48 0.032
## 1000 meter buffer 665.4 5.63 0.030
## 4750 meter buffer 665.7 5.90 0.026
## 3500 meter buffer 666.1 6.36 0.021
## 1250 meter buffer 666.5 6.68 0.018
## 5000 meter buffer 666.5 6.76 0.017
## 3750 meter buffer 666.7 6.94 0.016
## 2500 meter buffer 667.0 7.26 0.013
## 2000 meter buffer 667.5 7.74 0.011
## 2750 meter buffer 667.7 7.97 0.009
## 1500 meter buffer 667.8 7.98 0.009
## 2250 meter buffer 667.8 8.02 0.009
## 3250 meter buffer 668.6 8.87 0.006
## 3000 meter buffer 669.4 9.58 0.004
## 1750 meter buffer 670.4 10.61 0.003
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)this looks much more realistic; the 500 m buffer is top model for black bears
So what we will do for each species is remove the 250 meter buffer for now since there are some data missing. and compare just the other buffer sizes that contain the full data set
Model summary
Let’s take a look at the model summary for the top model
summary(black_bear_mods_no250$`500 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(black_bear, absent_black_bear) ~ seismic_lines + pipeline +
## borrowpits + wellsites + roads + trails + lc_class20 + lc_class34 +
## lc_class50 + lc_class110 + lc_class210 + lc_class220 + lc_class230 +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 661.4 706.7 -315.7 631.4 137
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.03008 0.1735
## Number of obs: 152, groups: array, 4
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3705 1.6088 -0.230 0.818
## seismic_lines -0.1239 0.3770 -0.329 0.742
## pipeline -0.4010 0.5776 -0.694 0.488
## borrowpits -2.7317 3.5769 -0.764 0.445
## wellsites 0.4296 0.7900 0.544 0.587
## roads -0.1306 0.5173 -0.252 0.801
## trails -0.1006 0.4442 -0.226 0.821
## lc_class20 -0.6703 1.8809 -0.356 0.722
## lc_class34 -0.6064 1.7885 -0.339 0.735
## lc_class50 -0.4908 1.5642 -0.314 0.754
## lc_class110 -0.6889 1.5636 -0.441 0.660
## lc_class210 -0.2043 1.5477 -0.132 0.895
## lc_class220 1.2969 1.5961 0.813 0.416
## lc_class230 -0.1915 1.5672 -0.122 0.903Let’s repeat this process for each species that we have enough data for.
Caribou
We may or may not have enough data for caribou but let’s try it at least for this preliminary report
We can use the same code from black beasr (above) to run global models for each buffer width except remember we want to remove 250 meters
And in the same chunk to save time let’s also run the
model.sel() function
caribou_mods_no250 <- final_df %>%
# remove 350 meter buffer width
purrr::discard_at('250 meter buffer') %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(caribou, absent_caribou) ~
seismic_lines +
pipeline +
borrowpits +
wellsites +
roads +
trails +
lc_class20 +
lc_class34 +
lc_class50 +
lc_class110 +
lc_class210 +
lc_class220 +
lc_class230 +
(1|array),
data = .,
family = 'binomial'))## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')
## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; non-positive-definite Hessian matrix. See vignette('troubleshooting')
## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; non-positive-definite Hessian matrix. See vignette('troubleshooting')
## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; non-positive-definite Hessian matrix. See vignette('troubleshooting')
## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; non-positive-definite Hessian matrix. See vignette('troubleshooting')# model selection
model.sel(caribou_mods_no250)## Model selection table
## cnd((Int)) dsp((Int)) cnd(brr) cnd(lc_c11) cnd(lc_c20)
## 4250 meter buffer -27.510 + -2.572 10.680 23.580
## 5000 meter buffer -26.110 + -14.020 11.220 28.530
## 3250 meter buffer -54.520 + 5.926 45.440 48.940
## 3750 meter buffer -22.970 + 1.763 18.410 18.890
## 2500 meter buffer -55.780 + -17.250 49.990 43.490
## 4000 meter buffer -23.790 + -2.363 12.090 18.610
## 1000 meter buffer -49.330 + 1.913 38.170 38.320
## 2250 meter buffer -37.490 + 10.600 29.720 28.270
## 1250 meter buffer -35.560 + -17.040 28.720 29.010
## 750 meter buffer -32.650 + 7.003 21.360 31.990
## 2000 meter buffer -46.300 + -7.312 37.500 38.610
## 1500 meter buffer -35.700 + -1.047 26.290 32.700
## 1750 meter buffer -38.510 + -10.180 33.360 29.810
## 2750 meter buffer -37.530 + 4.207 30.330 29.170
## 500 meter buffer -7.754 + 16.820 -2.579 5.773
## 3000 meter buffer -45.530 + 2.298 37.320 38.900
## 3500 meter buffer -63.670 + -13.310 54.360 59.490
## 4500 meter buffer -21.800 + 13.100 7.141 18.740
## 4750 meter buffer -25.250 + -9.999 8.156 28.600
## cnd(lc_c21) cnd(lc_c22) cnd(lc_c23) cnd(lc_c34) cnd(lc_c50)
## 4250 meter buffer 13.220 -6.7470 9.5450 78.5300 13.4800
## 5000 meter buffer 16.220 -5.4270 17.5100 73.8200 16.2600
## 3250 meter buffer 46.600 27.9800 47.0200 76.6500 47.4500
## 3750 meter buffer 19.020 -3.2270 11.3100 31.1700 15.4400
## 2500 meter buffer 52.550 37.0000 42.0200 70.4000 52.3200
## 4000 meter buffer 14.750 -4.9520 8.2450 64.7000 12.7800
## 1000 meter buffer 46.540 36.1700 31.9900 33.9900 40.6700
## 2250 meter buffer 34.300 18.0700 25.1000 42.9400 29.5900
## 1250 meter buffer 34.540 26.7800 23.4700 31.5500 31.6300
## 750 meter buffer 27.740 26.4000 18.3100 20.8200 25.6600
## 2000 meter buffer 42.080 29.6500 33.9100 48.4000 39.6300
## 1500 meter buffer 29.300 26.5500 28.3300 39.7500 32.3900
## 1750 meter buffer 34.380 26.0100 27.4400 38.6000 34.6200
## 2750 meter buffer 32.490 20.6700 28.2200 60.1800 32.8600
## 500 meter buffer 1.668 -0.3208 -0.2206 0.5675 0.7308
## 3000 meter buffer 39.700 23.6100 39.3800 70.7300 39.0800
## 3500 meter buffer 55.940 34.6700 51.7100 74.0700 55.0100
## 4500 meter buffer 6.660 -7.1570 5.4240 58.7800 9.9760
## 4750 meter buffer 12.710 -6.9930 11.5200 74.1400 12.8000
## cnd(ppl) cnd(rds) cnd(ssm_lns) cnd(trl) cnd(wll) df logLik
## 4250 meter buffer -14.980 19.870 14.09000 13.890 29.300 15 -65.472
## 5000 meter buffer -7.281 14.060 10.08000 8.032 26.300 15 -69.010
## 3250 meter buffer -4.064 9.023 5.37200 7.514 10.280 15 -69.014
## 3750 meter buffer -5.339 8.001 6.09400 8.063 3.877 15 -69.515
## 2500 meter buffer -15.920 5.627 1.52100 4.422 14.000 15 -69.588
## 4000 meter buffer -6.043 14.070 10.27000 12.300 11.120 15 -71.099
## 1000 meter buffer -13.330 7.164 3.25400 4.758 11.760 15 -72.230
## 2250 meter buffer -9.221 6.348 4.43300 7.423 1.271 15 -72.657
## 1250 meter buffer -18.310 1.743 -1.00500 2.149 7.739 15 -73.057
## 750 meter buffer -673.800 4.845 3.45500 2.515 2.598 15 -73.902
## 2000 meter buffer -6.345 6.081 2.94200 7.057 3.301 15 -74.197
## 1500 meter buffer -13.130 1.353 -0.80090 3.410 9.249 15 -74.866
## 1750 meter buffer -6.629 2.111 0.09778 4.664 2.160 15 -74.960
## 2750 meter buffer -4.771 4.033 2.13800 6.000 1.521 15 -75.182
## 500 meter buffer -1084.000 2.307 2.26100 4.144 -1.979 15 -88.060
## 3000 meter buffer -6.259 7.011 4.02500 6.854 5.822 15
## 3500 meter buffer -1.937 12.200 7.68700 8.994 13.840 15
## 4500 meter buffer -30.210 17.220 12.90000 9.281 39.010 15
## 4750 meter buffer -18.350 17.540 12.63000 9.146 41.030 15
## AICc delta
## 4250 meter buffer 164.5 0.00
## 5000 meter buffer 171.5 7.08
## 3250 meter buffer 171.6 7.08
## 3750 meter buffer 172.6 8.08
## 2500 meter buffer 172.7 8.23
## 4000 meter buffer 175.7 11.25
## 1000 meter buffer 178.0 13.52
## 2250 meter buffer 178.8 14.37
## 1250 meter buffer 179.6 15.17
## 750 meter buffer 181.3 16.86
## 2000 meter buffer 181.9 17.45
## 1500 meter buffer 183.3 18.79
## 1750 meter buffer 183.4 18.97
## 2750 meter buffer 183.9 19.42
## 500 meter buffer 209.6 45.17
## 3000 meter buffer
## 3500 meter buffer
## 4500 meter buffer
## 4750 meter buffer
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)We get a warning that there are some model convergence problems, I expect this is because we don’t have enough data for caribou but I don’t have time to dig into this now so we will investigate more closely for final analysis
For caribou 1250m buffer is top model for now
Let’s take a closer look at the top model summary
summary(caribou_mods_no250$`1250 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(caribou, absent_caribou) ~ seismic_lines + pipeline + borrowpits +
## wellsites + roads + trails + lc_class20 + lc_class34 + lc_class50 +
## lc_class110 + lc_class210 + lc_class220 + lc_class230 + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 176.1 221.5 -73.1 146.1 137
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 7.538 2.746
## Number of obs: 152, groups: array, 4
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -35.563 11.737 -3.030 0.00245 **
## seismic_lines -1.005 1.935 -0.519 0.60351
## pipeline -18.310 7.026 -2.606 0.00916 **
## borrowpits -17.036 13.559 -1.256 0.20894
## wellsites 7.739 4.257 1.818 0.06911 .
## roads 1.743 2.439 0.715 0.47485
## trails 2.149 2.523 0.852 0.39436
## lc_class20 29.014 12.595 2.304 0.02124 *
## lc_class34 31.546 11.174 2.823 0.00475 **
## lc_class50 31.628 11.987 2.639 0.00833 **
## lc_class110 28.725 11.951 2.404 0.01624 *
## lc_class210 34.536 12.811 2.696 0.00702 **
## lc_class220 26.785 11.672 2.295 0.02174 *
## lc_class230 23.469 11.507 2.040 0.04139 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1There’s nothing that catches my eye immediately as being sus about this particular model so it may not have been one with convergence issues. We will keep it in report for now
Coyote
coyote_mods_no250 <- final_df %>%
# remove 350 meter buffer width
purrr::discard_at('250 meter buffer') %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(coyote, absent_coyote) ~
seismic_lines +
pipeline +
borrowpits +
wellsites +
roads +
trails +
lc_class20 +
lc_class34 +
lc_class50 +
lc_class110 +
lc_class210 +
lc_class220 +
lc_class230 +
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(coyote_mods_no250)## Model selection table
## cnd((Int)) dsp((Int)) cnd(brr) cnd(lc_c11) cnd(lc_c20)
## 4750 meter buffer -0.08495 + -10.16000 -0.2466 5.790
## 5000 meter buffer -0.21630 + -8.75800 0.1736 5.630
## 4500 meter buffer -1.47500 + -12.23000 0.0884 7.496
## 4250 meter buffer -2.21600 + -7.04100 0.9704 8.918
## 3000 meter buffer -3.11000 + -10.63000 2.4960 6.791
## 2750 meter buffer -1.59100 + -8.08900 1.2310 6.159
## 3500 meter buffer -2.12900 + -5.66000 1.3850 6.319
## 3250 meter buffer -3.29900 + -8.29300 2.3430 7.866
## 4000 meter buffer -2.76500 + -0.01952 2.1350 8.384
## 2500 meter buffer -3.37600 + -8.08200 2.5140 6.989
## 3750 meter buffer -3.07500 + -2.89100 2.3580 6.734
## 2250 meter buffer -2.43700 + -3.13700 1.6200 5.359
## 1750 meter buffer -3.05600 + -4.40500 0.7562 6.774
## 1500 meter buffer -1.16400 + -5.16200 -0.9758 3.042
## 2000 meter buffer -1.69600 + -2.50300 -0.2595 4.830
## 1250 meter buffer -1.50700 + -1.89200 -0.3376 3.273
## 500 meter buffer 3.04700 + -11.87000 -4.1530 -3.354
## 1000 meter buffer -0.92770 + -2.30100 -0.6898 2.190
## 750 meter buffer 0.80740 + 0.55220 -2.3420 -3.307
## cnd(lc_c21) cnd(lc_c22) cnd(lc_c23) cnd(lc_c34) cnd(lc_c50)
## 4750 meter buffer 5.9710 2.89600 -6.603 8.9250 -1.2880
## 5000 meter buffer 6.6510 3.32100 -6.293 7.9440 -0.7587
## 4500 meter buffer 6.8050 3.39300 -5.209 13.2700 -0.2868
## 4250 meter buffer 6.0770 1.84200 -3.960 -5.1260 -0.5428
## 3000 meter buffer 7.2650 4.32100 -5.319 -4.7030 0.8320
## 2750 meter buffer 4.4880 1.25100 -5.400 -12.3500 -0.9980
## 3500 meter buffer 5.3470 0.93400 -5.181 -10.4200 -0.9148
## 3250 meter buffer 6.7610 2.39800 -4.559 -8.6000 0.2598
## 4000 meter buffer 6.0260 0.63670 -2.605 -32.8000 -0.8201
## 2500 meter buffer 5.3330 2.92300 -4.418 -6.2550 0.4718
## 3750 meter buffer 6.1540 1.38400 -3.555 -14.0500 -0.1334
## 2250 meter buffer 4.1120 1.09100 -3.254 -7.7280 -0.3658
## 1750 meter buffer 1.9430 1.53400 -1.629 -0.6329 -0.3105
## 1500 meter buffer 0.3031 -0.95300 -3.445 -2.6330 -2.9080
## 2000 meter buffer 2.0460 0.04994 -3.122 -4.0260 -1.3090
## 1250 meter buffer -0.1270 -0.35720 -2.318 -2.9060 -1.8130
## 500 meter buffer -4.0790 -4.09700 -3.968 -3.3830 -4.7730
## 1000 meter buffer -0.5428 -0.63960 -1.230 -0.5333 -1.6720
## 750 meter buffer -2.2730 -1.87100 -3.069 -0.4518 -2.6920
## cnd(ppl) cnd(rds) cnd(ssm_lns) cnd(trl) cnd(wll) df logLik
## 4750 meter buffer 2.05600 -1.81500 -0.999200 -6.62800 4.1390 15 -299.115
## 5000 meter buffer 1.38100 -2.21200 -1.348000 -7.12300 4.5840 15 -300.974
## 4500 meter buffer 2.62000 -0.94760 -0.540000 -5.50800 4.0930 15 -305.186
## 4250 meter buffer -0.03671 0.72540 0.731600 -4.59300 6.3700 15 -309.746
## 3000 meter buffer 2.47400 0.45800 -0.003364 -2.42000 2.6550 15 -312.922
## 2750 meter buffer 1.93500 0.90430 0.451700 -2.22000 3.2820 15 -313.881
## 3500 meter buffer 0.21890 1.39600 1.255000 -2.75000 5.1700 15 -314.162
## 3250 meter buffer 1.40200 1.60100 1.093000 -1.93800 3.0620 15 -314.444
## 4000 meter buffer -0.77580 1.60200 1.544000 -3.12100 5.6220 15 -314.804
## 2500 meter buffer 2.83200 1.73400 0.936700 -1.11700 0.5833 15 -315.340
## 3750 meter buffer -0.08756 1.61400 1.363000 -2.78700 5.5600 15 -315.561
## 2250 meter buffer 2.34200 1.55500 0.536100 -1.01400 0.9714 15 -321.930
## 1750 meter buffer 3.40000 2.40400 0.993400 -0.08169 0.8029 15 -323.037
## 1500 meter buffer 2.45200 2.80400 1.233000 0.15170 1.9900 15 -325.175
## 2000 meter buffer 2.46400 1.82100 0.691800 -0.65260 1.9360 15 -327.232
## 1250 meter buffer 2.25200 2.12100 1.100000 0.06838 1.4950 15 -331.548
## 500 meter buffer 0.41960 0.09539 -0.693400 0.04604 1.3450 15 -336.563
## 1000 meter buffer 1.86600 1.38800 0.365400 -0.41380 0.1284 15 -340.370
## 750 meter buffer 1.09100 0.83070 -0.295400 0.04470 1.6400 15 -341.549
## AICc delta weight
## 4750 meter buffer 631.8 0.00 0.863
## 5000 meter buffer 635.5 3.72 0.135
## 4500 meter buffer 643.9 12.14 0.002
## 4250 meter buffer 653.0 21.26 0.000
## 3000 meter buffer 659.4 27.61 0.000
## 2750 meter buffer 661.3 29.53 0.000
## 3500 meter buffer 661.9 30.09 0.000
## 3250 meter buffer 662.4 30.66 0.000
## 4000 meter buffer 663.1 31.38 0.000
## 2500 meter buffer 664.2 32.45 0.000
## 3750 meter buffer 664.7 32.89 0.000
## 2250 meter buffer 677.4 45.63 0.000
## 1750 meter buffer 679.6 47.84 0.000
## 1500 meter buffer 683.9 52.12 0.000
## 2000 meter buffer 688.0 56.23 0.000
## 1250 meter buffer 696.6 64.87 0.000
## 500 meter buffer 706.7 74.90 0.000
## 1000 meter buffer 714.3 82.51 0.000
## 750 meter buffer 716.6 84.87 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)for coyote top model appears to be 4500 m by quite a bit
Let’s get the model summary for this model
summary(coyote_mods_no250$`4500 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(coyote, absent_coyote) ~ seismic_lines + pipeline + borrowpits +
## wellsites + roads + trails + lc_class20 + lc_class34 + lc_class50 +
## lc_class110 + lc_class210 + lc_class220 + lc_class230 + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 640.4 685.7 -305.2 610.4 137
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 3.067e-10 1.751e-05
## Number of obs: 152, groups: array, 4
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.4754 2.5310 -0.583 0.5599
## seismic_lines -0.5400 1.0561 -0.511 0.6091
## pipeline 2.6202 1.6554 1.583 0.1135
## borrowpits -12.2269 6.0760 -2.012 0.0442 *
## wellsites 4.0932 2.7281 1.500 0.1335
## roads -0.9476 1.1313 -0.838 0.4022
## trails -5.5084 1.1641 -4.732 2.22e-06 ***
## lc_class20 7.4961 3.6399 2.059 0.0395 *
## lc_class34 13.2692 14.4421 0.919 0.3582
## lc_class50 -0.2868 2.4402 -0.118 0.9064
## lc_class110 0.0884 2.4431 0.036 0.9711
## lc_class210 6.8048 2.7885 2.440 0.0147 *
## lc_class220 3.3933 2.3817 1.425 0.1542
## lc_class230 -5.2090 2.5779 -2.021 0.0433 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1There is one lc class with a very high estimate and SE which seems a bit sus to me
Fisher
fisher_mods_no250 <- final_df %>%
# remove 350 meter buffer width
purrr::discard_at('250 meter buffer') %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(fisher, absent_fisher) ~
seismic_lines +
pipeline +
borrowpits +
wellsites +
roads +
trails +
lc_class20 +
lc_class34 +
lc_class50 +
lc_class110 +
lc_class210 +
lc_class220 +
lc_class230 +
(1|array),
data = .,
family = 'binomial'))## Warning in finalizeTMB(TMBStruc, obj, fit, h, data.tmb.old): Model convergence
## problem; singular convergence (7). See vignette('troubleshooting'),
## help('diagnose')# model selection
model.sel(fisher_mods_no250)## Model selection table
## cnd((Int)) dsp((Int)) cnd(brr) cnd(lc_c11) cnd(lc_c20)
## 2500 meter buffer 5.547 + -22.340 -5.56900 -6.332
## 2750 meter buffer 7.279 + -18.600 -7.12700 -6.177
## 2000 meter buffer 2.772 + -20.520 -3.01400 -3.670
## 3250 meter buffer 6.896 + -19.870 -7.10600 -4.005
## 2250 meter buffer 3.923 + -20.000 -4.24300 -3.839
## 3000 meter buffer 6.438 + -25.000 -7.37800 -4.165
## 1750 meter buffer 3.837 + -17.190 -4.64200 -5.103
## 3750 meter buffer 6.808 + -23.750 -6.71700 -4.844
## 1500 meter buffer 1.145 + -14.140 -1.38900 -2.486
## 500 meter buffer -206.300 + -3.105 203.20000 204.100
## 3500 meter buffer 7.742 + -15.450 -7.39800 -3.618
## 4000 meter buffer 4.646 + -21.930 -4.95100 -1.906
## 750 meter buffer -11.960 + -10.280 9.77700 10.670
## 4250 meter buffer 4.041 + -20.250 -3.99900 -3.637
## 1250 meter buffer -0.424 + -10.130 -0.07718 -1.525
## 4750 meter buffer 4.608 + -12.620 -3.71200 -6.128
## 4500 meter buffer 3.998 + -14.550 -3.68300 -6.425
## 5000 meter buffer 5.774 + -9.895 -4.31000 -6.296
## 1000 meter buffer -2.659 + -5.299 0.54330 2.036
## cnd(lc_c21) cnd(lc_c22) cnd(lc_c23) cnd(lc_c34) cnd(lc_c50)
## 2500 meter buffer -7.9120 -8.0340 -4.457 -4.5200 -5.8080
## 2750 meter buffer -9.6060 -10.1400 -4.994 -15.8800 -7.0410
## 2000 meter buffer -5.4840 -4.1790 -5.517 -3.6230 -2.5970
## 3250 meter buffer -10.6300 -9.1580 -4.190 -19.9300 -7.8010
## 2250 meter buffer -7.1950 -5.1290 -4.658 -2.8610 -3.9040
## 3000 meter buffer -10.3000 -9.1890 -5.101 -11.5900 -7.5710
## 1750 meter buffer -6.7840 -5.8860 -4.930 -4.9150 -3.9730
## 3750 meter buffer -8.9080 -9.7550 -5.344 -25.5400 -7.9620
## 1500 meter buffer -4.3270 -3.5170 -3.169 -3.1480 -1.4970
## 500 meter buffer 202.8000 204.1000 202.100 204.3000 203.1000
## 3500 meter buffer -9.9350 -9.5030 -5.713 -28.4700 -8.8960
## 4000 meter buffer -5.5530 -8.6370 -3.874 -26.3800 -6.1570
## 750 meter buffer 10.1900 9.3110 9.756 9.9900 9.4750
## 4250 meter buffer -4.4600 -8.1490 -3.317 -22.8000 -4.9060
## 1250 meter buffer -2.7670 -2.5670 -1.689 -0.8512 -0.1994
## 4750 meter buffer -5.7610 -7.7440 -2.656 -26.0200 -5.4220
## 4500 meter buffer -4.1480 -8.2130 -3.430 -19.3000 -4.5210
## 5000 meter buffer -6.4240 -8.5620 -3.544 -42.4300 -6.4730
## 1000 meter buffer -0.2956 -0.7572 -0.178 1.7690 1.0030
## cnd(ppl) cnd(rds) cnd(ssm_lns) cnd(trl) cnd(wll) df logLik
## 2500 meter buffer 0.5878 -3.0710 -3.646 -2.0590 0.93200 15 -168.507
## 2750 meter buffer 0.5857 -3.6490 -4.003 -2.3110 0.41190 15 -168.639
## 2000 meter buffer 2.0200 -2.4280 -2.964 -2.1210 0.55590 15 -168.928
## 3250 meter buffer 1.6390 -3.2260 -3.290 -1.3580 -2.33000 15 -170.390
## 2250 meter buffer 1.4420 -2.7610 -3.150 -2.4170 0.20680 15 -170.610
## 3000 meter buffer 1.3450 -2.1540 -2.697 -0.7910 -1.74200 15 -170.743
## 1750 meter buffer 0.5733 -2.4750 -2.912 -2.2060 3.06800 15 -170.803
## 3750 meter buffer 1.9710 -2.8770 -3.187 -0.9760 -2.16000 15 -171.232
## 1500 meter buffer 1.5190 -1.9160 -2.835 -0.6800 -0.38500 15 -171.327
## 500 meter buffer 1.3750 0.1708 0.206 -0.3670 -0.19210 15 -171.549
## 3500 meter buffer 1.9290 -3.5470 -3.516 -1.9240 -3.04500 15 -172.047
## 4000 meter buffer 3.1890 -2.6640 -2.601 -0.8963 -3.51000 15 -172.099
## 750 meter buffer 0.9276 -1.3280 -1.354 -0.8490 1.64000 15 -172.508
## 4250 meter buffer 3.5880 -3.0850 -3.155 -0.6601 -4.18600 15 -172.554
## 1250 meter buffer 0.9799 -1.7590 -2.175 -0.5384 0.02037 15 -173.142
## 4750 meter buffer 6.8560 -3.8530 -3.420 -0.3058 -11.76000 15 -174.034
## 4500 meter buffer 2.9530 -3.2190 -3.325 -0.7649 -3.86300 15 -174.210
## 5000 meter buffer 3.7570 -4.4220 -3.850 -1.5630 -5.56700 15 -174.883
## 1000 meter buffer 0.4348 -0.9629 -1.086 -0.5941 1.69100 15 -175.884
## AICc delta weight
## 2500 meter buffer 370.5 0.00 0.303
## 2750 meter buffer 370.8 0.26 0.266
## 2000 meter buffer 371.4 0.84 0.199
## 3250 meter buffer 374.3 3.77 0.046
## 2250 meter buffer 374.8 4.21 0.037
## 3000 meter buffer 375.0 4.47 0.032
## 1750 meter buffer 375.1 4.59 0.030
## 3750 meter buffer 376.0 5.45 0.020
## 1500 meter buffer 376.2 5.64 0.018
## 500 meter buffer 376.6 6.08 0.014
## 3500 meter buffer 377.6 7.08 0.009
## 4000 meter buffer 377.7 7.18 0.008
## 750 meter buffer 378.5 8.00 0.006
## 4250 meter buffer 378.6 8.09 0.005
## 1250 meter buffer 379.8 9.27 0.003
## 4750 meter buffer 381.6 11.05 0.001
## 4500 meter buffer 381.9 11.41 0.001
## 5000 meter buffer 383.3 12.75 0.001
## 1000 meter buffer 385.3 14.75 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)For fisher top model is 2000 meter
Let’s print the summary for this model
summary(fisher_mods_no250$`2000 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(fisher, absent_fisher) ~ seismic_lines + pipeline + borrowpits +
## wellsites + roads + trails + lc_class20 + lc_class34 + lc_class50 +
## lc_class110 + lc_class210 + lc_class220 + lc_class230 + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 367.9 413.2 -168.9 337.9 137
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 1.783 1.335
## Number of obs: 152, groups: array, 4
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.7721 2.5511 1.087 0.2772
## seismic_lines -2.9635 1.0988 -2.697 0.0070 **
## pipeline 2.0200 1.4830 1.362 0.1732
## borrowpits -20.5174 9.3253 -2.200 0.0278 *
## wellsites 0.5559 2.5248 0.220 0.8257
## roads -2.4279 1.4090 -1.723 0.0849 .
## trails -2.1214 1.1269 -1.883 0.0598 .
## lc_class20 -3.6696 2.8889 -1.270 0.2040
## lc_class34 -3.6234 7.2996 -0.496 0.6196
## lc_class50 -2.5967 2.2342 -1.162 0.2451
## lc_class110 -3.0135 2.4184 -1.246 0.2127
## lc_class210 -5.4838 2.8300 -1.938 0.0527 .
## lc_class220 -4.1786 2.4971 -1.673 0.0943 .
## lc_class230 -5.5168 2.5162 -2.192 0.0283 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Again lc_class34 has a very high standard error, we may not have enough data in this landcover class to use in the final analysis
Grey wolf
wolf_mods_no250 <- final_df %>%
# remove 350 meter buffer width
purrr::discard_at('250 meter buffer') %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(grey_wolf, absent_grey_wolf) ~
seismic_lines +
pipeline +
borrowpits +
wellsites +
roads +
trails +
lc_class20 +
lc_class34 +
lc_class50 +
lc_class110 +
lc_class210 +
lc_class220 +
lc_class230 +
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(wolf_mods_no250)## Model selection table
## cnd((Int)) dsp((Int)) cnd(brr) cnd(lc_c11) cnd(lc_c20)
## 3750 meter buffer -10.510 + 18.8400 12.540 7.429
## 4000 meter buffer -10.300 + 19.4600 9.827 7.102
## 3250 meter buffer -14.660 + 22.6900 16.360 7.025
## 4500 meter buffer -6.936 + 12.2600 5.408 9.396
## 4250 meter buffer -5.790 + 15.0200 5.380 6.754
## 3000 meter buffer -15.640 + 18.8300 16.430 7.908
## 3500 meter buffer -12.600 + 22.6800 15.560 6.655
## 2750 meter buffer -13.670 + 24.7600 14.190 7.631
## 4750 meter buffer -9.296 + 4.2280 6.038 9.575
## 5000 meter buffer -9.007 + 0.6744 6.215 7.817
## 2500 meter buffer -11.530 + 17.8500 12.130 7.462
## 1750 meter buffer -13.220 + 10.1900 11.250 9.449
## 1250 meter buffer -11.180 + 6.6470 8.402 9.471
## 1500 meter buffer -10.410 + 6.1110 7.866 8.383
## 2000 meter buffer -12.200 + 10.8000 10.250 10.510
## 2250 meter buffer -12.430 + 14.9400 11.980 9.630
## 1000 meter buffer -7.422 + 19.3300 4.612 6.292
## 750 meter buffer -5.668 + 8.0980 2.376 2.684
## 500 meter buffer -5.779 + -9.0020 3.057 2.157
## cnd(lc_c21) cnd(lc_c22) cnd(lc_c23) cnd(lc_c34) cnd(lc_c50)
## 3750 meter buffer 13.000 5.047 6.369 -75.950 8.054
## 4000 meter buffer 13.090 4.363 5.602 -70.960 7.448
## 3250 meter buffer 16.790 9.845 10.600 -47.540 12.100
## 4500 meter buffer 8.556 2.473 3.475 -83.970 4.251
## 4250 meter buffer 7.508 1.306 2.788 -82.100 3.687
## 3000 meter buffer 16.710 9.927 12.690 -35.210 12.860
## 3500 meter buffer 14.910 8.070 10.060 -58.820 10.670
## 2750 meter buffer 14.040 8.286 11.330 -26.540 10.980
## 4750 meter buffer 8.955 4.202 4.422 -68.030 5.646
## 5000 meter buffer 9.197 2.776 4.305 -80.160 5.340
## 2500 meter buffer 10.680 6.564 8.132 -19.930 8.321
## 1750 meter buffer 12.050 6.682 7.176 -6.558 8.335
## 1250 meter buffer 9.470 5.231 5.503 -2.647 5.362
## 1500 meter buffer 8.308 4.247 4.828 -3.690 5.024
## 2000 meter buffer 11.420 6.183 6.063 -11.320 7.652
## 2250 meter buffer 11.170 7.697 8.091 -14.060 8.957
## 1000 meter buffer 4.182 3.625 3.463 -1.872 3.417
## 750 meter buffer 2.134 1.526 1.817 -1.649 1.567
## 500 meter buffer 2.405 2.151 1.530 1.168 1.817
## cnd(ppl) cnd(rds) cnd(ssm_lns) cnd(trl) cnd(wll) df logLik
## 3750 meter buffer -15.09000 0.10330 -0.09271 -2.2610 15.1000 15 -174.803
## 4000 meter buffer -15.41000 0.97660 0.60560 -1.1840 13.8500 15 -176.036
## 3250 meter buffer -12.11000 0.09448 -0.30210 -0.7732 9.0200 15 -176.746
## 4500 meter buffer -20.69000 0.58330 0.50590 -3.3870 22.5300 15 -178.915
## 4250 meter buffer -17.77000 -0.08114 0.09835 -3.6280 17.9600 15 -179.394
## 3000 meter buffer -9.50600 0.35300 -0.08657 -0.2605 8.4640 15 -179.732
## 3500 meter buffer -12.67000 -1.09100 -1.27100 -1.8420 11.0800 15 -180.010
## 2750 meter buffer -9.04700 -0.05575 -0.36520 -0.3487 7.4750 15 -181.317
## 4750 meter buffer -17.72000 2.34000 1.89700 -1.2780 21.2300 15 -184.111
## 5000 meter buffer -15.40000 2.43200 1.84400 -0.3963 19.0600 15 -185.542
## 2500 meter buffer -7.58600 0.58260 0.11670 0.6559 4.7480 15 -186.923
## 1750 meter buffer -4.38400 3.00900 2.39100 1.5010 2.4770 15 -187.397
## 1250 meter buffer -3.15700 3.50500 2.39800 1.4510 1.2450 15 -187.748
## 1500 meter buffer -3.96700 3.33900 2.32400 1.5970 3.0150 15 -188.914
## 2000 meter buffer -5.03600 2.70000 1.97100 1.1880 4.2230 15 -190.436
## 2250 meter buffer -6.08400 1.28100 0.73260 0.8988 2.7970 15 -190.880
## 1000 meter buffer 0.03988 0.36850 1.22600 1.5110 -1.8270 15 -194.098
## 750 meter buffer 0.43300 1.77100 1.41300 1.2110 -0.6427 15 -200.541
## 500 meter buffer 0.80290 1.75000 0.98560 1.0630 -0.7946 15 -200.707
## AICc delta weight
## 3750 meter buffer 383.1 0.00 0.678
## 4000 meter buffer 385.6 2.47 0.197
## 3250 meter buffer 387.0 3.89 0.097
## 4500 meter buffer 391.4 8.22 0.011
## 4250 meter buffer 392.3 9.18 0.007
## 3000 meter buffer 393.0 9.86 0.005
## 3500 meter buffer 393.5 10.41 0.004
## 2750 meter buffer 396.2 13.03 0.001
## 4750 meter buffer 401.8 18.62 0.000
## 5000 meter buffer 404.6 21.48 0.000
## 2500 meter buffer 407.4 24.24 0.000
## 1750 meter buffer 408.3 25.19 0.000
## 1250 meter buffer 409.0 25.89 0.000
## 1500 meter buffer 411.4 28.22 0.000
## 2000 meter buffer 414.4 31.27 0.000
## 2250 meter buffer 415.3 32.16 0.000
## 1000 meter buffer 421.7 38.59 0.000
## 750 meter buffer 434.6 51.48 0.000
## 500 meter buffer 434.9 51.81 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)For grey wolf top model is 4500 m buffer
Let’s get the model summary for this model
summary(wolf_mods_no250$`4500 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(grey_wolf, absent_grey_wolf) ~ seismic_lines + pipeline +
## borrowpits + wellsites + roads + trails + lc_class20 + lc_class34 +
## lc_class50 + lc_class110 + lc_class210 + lc_class220 + lc_class230 +
## (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 387.8 433.2 -178.9 357.8 137
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 4.188e-09 6.472e-05
## Number of obs: 152, groups: array, 4
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.9362 5.0762 -1.366 0.1718
## seismic_lines 0.5059 1.6039 0.315 0.7525
## pipeline -20.6864 3.8734 -5.341 9.26e-08 ***
## borrowpits 12.2580 9.7501 1.257 0.2087
## wellsites 22.5258 4.0771 5.525 3.29e-08 ***
## roads 0.5833 1.9442 0.300 0.7641
## trails -3.3871 1.9879 -1.704 0.0884 .
## lc_class20 9.3963 6.4449 1.458 0.1449
## lc_class34 -83.9732 26.9038 -3.121 0.0018 **
## lc_class50 4.2511 4.8758 0.872 0.3833
## lc_class110 5.4079 5.0393 1.073 0.2832
## lc_class210 8.5561 5.1606 1.658 0.0973 .
## lc_class220 2.4732 4.9505 0.500 0.6174
## lc_class230 3.4749 4.9971 0.695 0.4868
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lc_class34 still presenting some issues, interesting that seismic lines weren’t significant and have a negative estimate
Lynx
lynx_mods_no250 <- final_df %>%
# remove 350 meter buffer width
purrr::discard_at('250 meter buffer') %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(lynx, absent_lynx) ~
seismic_lines +
pipeline +
borrowpits +
wellsites +
roads +
trails +
lc_class20 +
lc_class34 +
lc_class50 +
lc_class110 +
lc_class210 +
lc_class220 +
lc_class230 +
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(lynx_mods_no250)## Model selection table
## cnd((Int)) dsp((Int)) cnd(brr) cnd(lc_c11) cnd(lc_c20)
## 750 meter buffer -7.2440 + 4.1720 5.3780 -3.860
## 1000 meter buffer -10.3500 + 2.0140 8.1860 2.235
## 1250 meter buffer -13.7800 + -1.9290 10.7600 12.920
## 500 meter buffer -1.6740 + -4.5310 -0.1358 -4.147
## 2500 meter buffer -15.9300 + -9.7760 12.2600 14.020
## 1750 meter buffer -14.3300 + 1.0280 11.1100 14.550
## 1500 meter buffer -15.0400 + -0.3430 11.3900 12.660
## 5000 meter buffer -2.3840 + -0.6378 -0.8068 -0.644
## 2750 meter buffer -14.3000 + -9.0810 10.9000 12.640
## 4750 meter buffer -0.1961 + 0.5363 -2.8660 -4.934
## 2000 meter buffer -14.0100 + -0.5195 10.6900 11.260
## 3250 meter buffer -6.6590 + -4.9000 4.0480 3.626
## 2250 meter buffer -14.1600 + -4.7400 11.2800 10.870
## 3500 meter buffer -4.5340 + -0.8624 1.8270 1.040
## 3000 meter buffer -9.9420 + -5.7680 7.1950 7.039
## 4500 meter buffer -2.1510 + -0.2477 -0.9033 -2.676
## 3750 meter buffer -2.9910 + -1.2510 0.6628 -1.126
## 4000 meter buffer -0.6971 + -1.1460 -0.6745 -5.145
## 4250 meter buffer -0.5769 + -0.6674 -1.3080 -5.281
## cnd(lc_c21) cnd(lc_c22) cnd(lc_c23) cnd(lc_c34) cnd(lc_c50)
## 750 meter buffer 5.7510 4.8420 3.5120 3.6320 3.7990
## 1000 meter buffer 8.1460 7.4930 6.4920 8.3480 6.8070
## 1250 meter buffer 11.5600 10.2800 9.1820 9.0880 9.0930
## 500 meter buffer 0.5488 -0.6783 -1.1560 -1.5890 -1.3020
## 2500 meter buffer 11.8900 10.8500 9.6450 2.2140 10.3800
## 1750 meter buffer 11.8000 10.3500 9.4540 4.1960 8.9840
## 1500 meter buffer 13.4700 11.4900 9.6920 9.5520 10.4900
## 5000 meter buffer -0.1329 -1.1260 -3.8710 25.3300 -1.5540
## 2750 meter buffer 10.3800 10.3500 9.1990 8.3780 10.4100
## 4750 meter buffer -2.4280 -2.8490 -5.3280 20.5100 -3.4740
## 2000 meter buffer 10.9300 9.1630 8.8460 3.1450 8.0030
## 3250 meter buffer 2.5390 3.4900 1.8720 11.9400 3.8980
## 2250 meter buffer 10.4000 9.6760 9.3810 0.3421 8.9210
## 3500 meter buffer 1.2710 1.4870 -0.3037 11.6000 1.6080
## 3000 meter buffer 5.2430 6.9300 4.9650 10.1700 6.6630
## 4500 meter buffer -0.3603 -1.5210 -2.9030 18.0600 -1.0670
## 3750 meter buffer -0.2613 -0.3054 -1.4640 9.3620 0.1612
## 4000 meter buffer -2.6760 -2.2500 -2.3350 11.4500 -1.8060
## 4250 meter buffer -2.2900 -3.0090 -3.2680 11.1100 -2.0510
## cnd(ppl) cnd(rds) cnd(ssm_lns) cnd(trl) cnd(wll) df logLik
## 750 meter buffer 1.0810 2.0560 0.48590 0.4565 1.1230 15 -288.714
## 1000 meter buffer 2.5290 2.2420 0.91990 1.1600 -1.0590 15 -293.455
## 1250 meter buffer 3.3230 2.8570 1.61800 2.1720 -0.9282 15 -293.460
## 500 meter buffer -0.6344 0.9736 0.04477 -0.1048 2.9010 15 -295.259
## 2500 meter buffer 3.2780 4.5130 3.82400 2.9140 3.8330 15 -295.629
## 1750 meter buffer 2.3060 3.0990 2.63300 2.3020 1.2800 15 -295.751
## 1500 meter buffer 2.2210 2.9480 1.93200 1.7180 2.0870 15 -296.296
## 5000 meter buffer -6.6520 2.5920 2.29900 -0.7819 17.4400 15 -296.903
## 2750 meter buffer 3.0290 3.0410 2.57200 1.0790 5.0200 15 -297.569
## 4750 meter buffer -6.1050 2.1790 1.97500 -0.6911 15.1400 15 -298.569
## 2000 meter buffer 2.2040 3.8400 3.30900 3.3810 1.9170 15 -298.671
## 3250 meter buffer -1.4270 2.0360 2.12900 -0.7995 8.9050 15 -299.136
## 2250 meter buffer 3.9250 3.5440 2.83800 3.0790 1.8860 15 -299.400
## 3500 meter buffer -2.2590 1.7650 2.04700 -1.1090 9.6540 15 -301.128
## 3000 meter buffer 2.0190 2.3400 2.26800 0.5232 4.8820 15 -301.414
## 4500 meter buffer -3.3690 2.0510 1.96500 -0.5899 11.2200 15 -302.073
## 3750 meter buffer -2.2100 1.6070 1.86400 -0.9372 9.3990 15 -302.805
## 4000 meter buffer -1.2260 0.6927 1.14300 -1.2400 7.7790 15 -302.955
## 4250 meter buffer -1.3800 1.1680 1.56700 -1.0140 7.7080 15 -303.274
## AICc delta weight
## 750 meter buffer 611.0 0.00 0.979
## 1000 meter buffer 620.4 9.48 0.009
## 1250 meter buffer 620.4 9.49 0.009
## 500 meter buffer 624.0 13.09 0.001
## 2500 meter buffer 624.8 13.83 0.001
## 1750 meter buffer 625.0 14.07 0.001
## 1500 meter buffer 626.1 15.16 0.000
## 5000 meter buffer 627.3 16.38 0.000
## 2750 meter buffer 628.7 17.71 0.000
## 4750 meter buffer 630.7 19.71 0.000
## 2000 meter buffer 630.9 19.91 0.000
## 3250 meter buffer 631.8 20.84 0.000
## 2250 meter buffer 632.3 21.37 0.000
## 3500 meter buffer 635.8 24.83 0.000
## 3000 meter buffer 636.4 25.40 0.000
## 4500 meter buffer 637.7 26.72 0.000
## 3750 meter buffer 639.1 28.18 0.000
## 4000 meter buffer 639.4 28.48 0.000
## 4250 meter buffer 640.1 29.12 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)For lynx the top model is the 1000 m buffer
Let’s get the model summary
summary(lynx_mods_no250$`1000 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(lynx, absent_lynx) ~ seismic_lines + pipeline + borrowpits +
## wellsites + roads + trails + lc_class20 + lc_class34 + lc_class50 +
## lc_class110 + lc_class210 + lc_class220 + lc_class230 + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 616.9 662.3 -293.5 586.9 137
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 0.009742 0.0987
## Number of obs: 152, groups: array, 4
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -10.3480 2.6475 -3.909 9.28e-05 ***
## seismic_lines 0.9199 0.5963 1.543 0.12289
## pipeline 2.5293 0.9589 2.638 0.00835 **
## borrowpits 2.0140 4.1551 0.485 0.62789
## wellsites -1.0595 1.3301 -0.797 0.42571
## roads 2.2419 0.8048 2.786 0.00534 **
## trails 1.1598 0.6942 1.671 0.09478 .
## lc_class20 2.2347 3.5463 0.630 0.52860
## lc_class34 8.3482 3.1569 2.644 0.00818 **
## lc_class50 6.8067 2.6463 2.572 0.01011 *
## lc_class110 8.1856 2.5943 3.155 0.00160 **
## lc_class210 8.1459 2.7213 2.993 0.00276 **
## lc_class220 7.4935 2.6936 2.782 0.00540 **
## lc_class230 6.4919 2.6612 2.439 0.01471 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Moose
moose_mods_no250 <- final_df %>%
# remove 350 meter buffer width
purrr::discard_at('250 meter buffer') %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(moose, absent_moose) ~
seismic_lines +
pipeline +
borrowpits +
wellsites +
roads +
trails +
lc_class20 +
lc_class34 +
lc_class50 +
lc_class110 +
lc_class210 +
lc_class220 +
lc_class230 +
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(moose_mods_no250)## Model selection table
## cnd((Int)) dsp((Int)) cnd(brr) cnd(lc_c11) cnd(lc_c20)
## 3750 meter buffer -0.18680 + 1.6060 -1.6770 -1.5320
## 500 meter buffer -5.31100 + -8.7360 4.9570 5.4540
## 4000 meter buffer -0.06881 + -3.5510 -1.6430 -2.0670
## 3250 meter buffer -1.86900 + 8.3310 0.7309 -0.9436
## 3000 meter buffer -0.85170 + 9.3060 -0.1209 -0.3379
## 2250 meter buffer -0.84560 + 10.1400 1.0920 0.8478
## 3500 meter buffer -0.08749 + 3.5810 -1.4530 -1.1310
## 750 meter buffer -4.78000 + -0.7357 2.9290 7.0660
## 2750 meter buffer -0.17090 + 11.7400 0.1404 -1.0460
## 2500 meter buffer -0.18740 + 11.5200 0.3941 -0.4472
## 4750 meter buffer 0.88060 + -10.3800 -2.7910 -3.6680
## 5000 meter buffer 2.04700 + -9.2220 -3.4390 -4.4980
## 4500 meter buffer 0.58590 + -6.9310 -2.2090 -3.4300
## 4250 meter buffer 0.89340 + -4.4540 -2.3180 -4.1220
## 2000 meter buffer -0.90940 + 4.3410 0.1203 0.5869
## 1750 meter buffer -1.74400 + 3.7490 1.2330 1.9700
## 1500 meter buffer -0.04846 + -1.8850 -0.3197 0.9663
## 1250 meter buffer -0.97140 + -3.1900 0.3438 1.9300
## 1000 meter buffer -1.12700 + -1.7150 0.4704 2.3250
## cnd(lc_c21) cnd(lc_c22) cnd(lc_c23) cnd(lc_c34) cnd(lc_c50)
## 3750 meter buffer -0.74180 -1.4910 0.1646 -19.5100 -1.40100
## 500 meter buffer 4.00100 5.6370 5.6360 4.5460 4.02900
## 4000 meter buffer 0.05318 -2.1260 -0.2459 -1.6980 -1.76700
## 3250 meter buffer 1.44200 0.7038 2.0560 -12.4800 0.21500
## 3000 meter buffer 0.43970 -0.7143 0.7540 -15.2600 -0.80070
## 2250 meter buffer 0.75530 -1.3000 1.5580 -15.6500 -0.42090
## 3500 meter buffer -0.70580 -1.3730 0.0586 -18.1800 -1.53500
## 750 meter buffer 4.06000 4.5610 4.1730 2.3200 2.94100
## 2750 meter buffer 0.05767 -1.3620 0.3643 -19.2800 -1.30400
## 2500 meter buffer 0.18610 -1.9870 0.1478 -18.5900 -1.43000
## 4750 meter buffer -0.62150 -3.0420 -0.4440 18.9700 -2.66500
## 5000 meter buffer -1.57000 -3.9060 -1.4270 21.9600 -3.53000
## 4500 meter buffer -0.77680 -2.6810 -0.3695 4.7680 -2.45700
## 4250 meter buffer -0.81380 -2.9140 -0.5764 1.7190 -2.32900
## 2000 meter buffer 0.03522 -0.7530 1.4490 -8.1510 -0.70010
## 1750 meter buffer 0.96740 0.4748 1.9070 -4.1270 -0.22920
## 1500 meter buffer -0.79890 -0.2145 -0.5274 -2.9790 -1.56800
## 1250 meter buffer -0.19010 0.6056 0.6409 -0.6084 -0.43920
## 1000 meter buffer -0.24210 1.0790 1.0800 0.6271 -0.01273
## cnd(ppl) cnd(rds) cnd(ssm_lns) cnd(trl) cnd(wll) df logLik
## 3750 meter buffer -6.291 -0.5856 1.00500 -0.09781 0.5362 15 -302.076
## 500 meter buffer -1.439 -1.2940 -0.64690 -0.98350 -0.4259 15 -302.597
## 4000 meter buffer -6.063 -0.2953 1.03600 -0.01724 -1.0620 15 -302.787
## 3250 meter buffer -6.637 -1.1550 0.43790 -0.32210 1.0850 15 -303.233
## 3000 meter buffer -7.115 -1.2020 0.60440 -0.82810 3.1200 15 -303.251
## 2250 meter buffer -5.015 -1.7170 -0.01713 -1.09600 1.1260 15 -303.374
## 3500 meter buffer -7.034 -0.7724 0.81050 -0.49360 2.0380 15 -303.837
## 750 meter buffer -1.838 -0.9000 -0.01174 -1.10100 -0.8452 15 -303.986
## 2750 meter buffer -6.737 -1.5230 0.16060 -1.12800 3.0270 15 -303.995
## 2500 meter buffer -5.769 -1.3710 0.30700 -0.75810 1.7990 15 -304.601
## 4750 meter buffer -5.573 -0.5700 0.66620 -0.08074 -0.5954 15 -304.864
## 5000 meter buffer -5.110 -1.2730 0.41010 -0.57930 -1.4990 15 -304.984
## 4500 meter buffer -4.892 -0.4925 0.87600 0.19800 -2.0140 15 -306.058
## 4250 meter buffer -4.950 -0.8266 0.55860 -0.13530 -2.2470 15 -306.128
## 2000 meter buffer -4.541 -1.1100 0.29120 -0.65190 0.7489 15 -306.878
## 1750 meter buffer -3.957 -1.0500 0.26310 -0.35570 -1.1090 15 -307.198
## 1500 meter buffer -4.002 -1.2080 -0.02248 -0.91600 -0.5908 15 -308.780
## 1250 meter buffer -2.796 -1.1330 -0.11170 -0.84400 -1.4450 15 -311.149
## 1000 meter buffer -2.204 -1.5150 -0.38660 -1.03500 -1.0020 15 -313.019
## AICc delta weight
## 3750 meter buffer 637.7 0.00 0.271
## 500 meter buffer 638.7 1.04 0.161
## 4000 meter buffer 639.1 1.42 0.133
## 3250 meter buffer 640.0 2.31 0.085
## 3000 meter buffer 640.0 2.35 0.084
## 2250 meter buffer 640.3 2.60 0.074
## 3500 meter buffer 641.2 3.52 0.047
## 750 meter buffer 641.5 3.82 0.040
## 2750 meter buffer 641.5 3.84 0.040
## 2500 meter buffer 642.7 5.05 0.022
## 4750 meter buffer 643.3 5.58 0.017
## 5000 meter buffer 643.5 5.82 0.015
## 4500 meter buffer 645.6 7.97 0.005
## 4250 meter buffer 645.8 8.10 0.005
## 2000 meter buffer 647.3 9.60 0.002
## 1750 meter buffer 647.9 10.24 0.002
## 1500 meter buffer 651.1 13.41 0.000
## 1250 meter buffer 655.8 18.15 0.000
## 1000 meter buffer 659.6 21.89 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)For moose the top model is the 3750 m buffer
Let’s get the model summary
summary(moose_mods_no250$`3750 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(moose, absent_moose) ~ seismic_lines + pipeline + borrowpits +
## wellsites + roads + trails + lc_class20 + lc_class34 + lc_class50 +
## lc_class110 + lc_class210 + lc_class220 + lc_class230 + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 634.2 679.5 -302.1 604.2 137
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 4.409e-10 2.1e-05
## Number of obs: 152, groups: array, 4
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.18680 2.73864 -0.068 0.945619
## seismic_lines 1.00450 0.88172 1.139 0.254598
## pipeline -6.29073 1.65572 -3.799 0.000145 ***
## borrowpits 1.60628 5.40801 0.297 0.766452
## wellsites 0.53622 2.16428 0.248 0.804319
## roads -0.58556 1.05508 -0.555 0.578901
## trails -0.09781 0.99686 -0.098 0.921839
## lc_class20 -1.53153 3.35843 -0.456 0.648373
## lc_class34 -19.51133 10.62742 -1.836 0.066366 .
## lc_class50 -1.40080 2.61503 -0.536 0.592186
## lc_class110 -1.67652 2.64796 -0.633 0.526646
## lc_class210 -0.74176 2.85993 -0.259 0.795354
## lc_class220 -1.49130 2.66783 -0.559 0.576168
## lc_class230 0.16455 2.59309 0.063 0.949401
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Red fox
fox_mods_no250 <- final_df %>%
# remove 350 meter buffer width
purrr::discard_at('250 meter buffer') %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
glmmTMB::glmmTMB(cbind(red_fox, absent_red_fox) ~
seismic_lines +
pipeline +
borrowpits +
wellsites +
roads +
trails +
lc_class20 +
lc_class34 +
lc_class50 +
lc_class110 +
lc_class210 +
lc_class220 +
lc_class230 +
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(fox_mods_no250)## Model selection table
## cnd((Int)) dsp((Int)) cnd(brr) cnd(lc_c11) cnd(lc_c20)
## 500 meter buffer -2.5350 + 8.045 0.30030 -4.2490
## 3250 meter buffer -1.8780 + -24.310 6.31300 0.3432
## 5000 meter buffer -4.2450 + -48.150 -0.01949 15.2500
## 4750 meter buffer -6.6770 + -50.640 2.17400 17.0500
## 3000 meter buffer -3.2410 + -36.230 8.11300 -0.6053
## 3500 meter buffer -1.0710 + -28.270 6.55400 -2.3710
## 4500 meter buffer -5.0310 + -45.170 3.16900 13.1900
## 4250 meter buffer -5.3540 + -36.060 3.83200 13.2200
## 3750 meter buffer -5.3600 + -44.530 5.79400 6.1910
## 4000 meter buffer -6.7640 + -34.860 5.17100 7.6620
## 2750 meter buffer -4.4030 + -34.330 6.49000 1.7550
## 750 meter buffer -4.8760 + -7.385 1.66500 0.2266
## 1000 meter buffer -0.3164 + 1.064 -3.20400 -3.8810
## 2250 meter buffer -7.0970 + -35.320 4.10400 2.7390
## 2500 meter buffer -2.7410 + -20.620 3.61600 -0.1645
## 2000 meter buffer -6.0340 + -20.030 4.07300 2.7720
## 1250 meter buffer -1.7950 + -8.857 -2.70400 -4.4450
## 1500 meter buffer -3.4350 + -5.641 0.97020 -1.8670
## 1750 meter buffer -3.3080 + -14.860 0.86500 -2.2250
## cnd(lc_c21) cnd(lc_c22) cnd(lc_c23) cnd(lc_c34) cnd(lc_c50)
## 500 meter buffer -7.5030 -3.8590 -4.6400 -5.5180 -5.2100
## 3250 meter buffer 5.7970 -3.8260 -0.1972 1.3640 -2.3330
## 5000 meter buffer 5.9290 -5.3100 -6.6610 5.8790 -4.2780
## 4750 meter buffer 6.5070 -3.3150 -4.0770 31.0900 -1.7330
## 3000 meter buffer 8.3460 -1.7820 0.4510 -7.3680 -1.7250
## 3500 meter buffer 3.9730 -4.1260 0.3306 9.0720 -2.3890
## 4500 meter buffer 5.5000 -3.3350 -4.6490 10.2500 -2.9750
## 4250 meter buffer 5.9440 -3.4510 -2.9620 23.9100 -2.3760
## 3750 meter buffer 4.9450 -1.6920 -0.3727 28.0600 -0.8399
## 4000 meter buffer 5.8850 -1.4440 -2.4020 32.9100 -0.5652
## 2750 meter buffer 7.6970 -0.7962 4.5920 -0.8732 1.0240
## 750 meter buffer -3.5300 1.0090 0.2439 -0.9965 1.1890
## 1000 meter buffer -6.3970 -6.8690 -5.2280 -4.5860 -4.3310
## 2250 meter buffer 4.9280 3.3350 4.1270 16.9000 2.7110
## 2500 meter buffer 2.9930 -1.1920 1.6660 -2.1310 -0.5620
## 2000 meter buffer 0.1995 2.8610 3.3670 11.0100 2.6440
## 1250 meter buffer -4.0600 -4.2180 -3.4020 1.4780 -2.8780
## 1500 meter buffer -2.0290 -1.1090 -0.9122 3.3400 0.2728
## 1750 meter buffer -0.5451 -1.0060 -1.8550 5.4800 0.1459
## cnd(ppl) cnd(rds) cnd(ssm_lns) cnd(trl) cnd(wll) df logLik
## 500 meter buffer 5.077 5.75000 3.9700 1.92000 -14.8600 15 -113.738
## 3250 meter buffer 7.644 -3.02600 -2.7870 -1.96100 -12.7800 15 -134.069
## 5000 meter buffer 4.249 4.23400 5.7080 -2.09700 0.6589 15 -134.197
## 4750 meter buffer 7.789 4.74200 5.8910 -0.41150 -5.8730 15 -134.722
## 3000 meter buffer 3.346 -2.58400 -2.9190 -2.14600 -4.0520 15 -134.936
## 3500 meter buffer 8.457 -3.99700 -3.1680 -2.06200 -15.9000 15 -135.792
## 4500 meter buffer 4.204 3.40000 3.9740 -0.71710 -1.5480 15 -136.499
## 4250 meter buffer 7.043 2.81500 2.8250 1.02400 -9.1310 15 -136.936
## 3750 meter buffer 8.756 1.02400 1.0380 1.21400 -12.9400 15 -137.141
## 4000 meter buffer 9.758 3.37400 3.1730 2.54600 -15.8300 15 -138.440
## 2750 meter buffer 4.948 -4.11700 -3.7050 -2.44900 -3.1040 15 -138.678
## 750 meter buffer 2.669 2.89800 1.3630 0.17270 3.8360 15 -139.781
## 1000 meter buffer 1.756 3.24100 1.8410 -1.06900 3.8370 15 -142.960
## 2250 meter buffer 6.604 -0.59570 -1.2960 -0.03987 -2.8970 15 -145.048
## 2500 meter buffer 5.403 -3.45200 -2.9160 -1.96200 -3.6040 15 -145.241
## 2000 meter buffer 8.459 -1.19700 -1.4540 1.12200 -8.6090 15 -145.514
## 1250 meter buffer 1.103 3.06200 0.6782 1.82400 2.2090 15 -150.979
## 1500 meter buffer 2.857 0.21900 -1.0510 1.86200 -2.1440 15 -152.307
## 1750 meter buffer 1.678 -0.01589 -0.8923 0.40610 1.0990 15 -153.758
## AICc delta weight
## 500 meter buffer 261.0 0.00 1
## 3250 meter buffer 301.7 40.66 0
## 5000 meter buffer 301.9 40.92 0
## 4750 meter buffer 303.0 41.97 0
## 3000 meter buffer 303.4 42.40 0
## 3500 meter buffer 305.1 44.11 0
## 4500 meter buffer 306.5 45.52 0
## 4250 meter buffer 307.4 46.40 0
## 3750 meter buffer 307.8 46.81 0
## 4000 meter buffer 310.4 49.40 0
## 2750 meter buffer 310.9 49.88 0
## 750 meter buffer 313.1 52.09 0
## 1000 meter buffer 319.5 58.45 0
## 2250 meter buffer 323.6 62.62 0
## 2500 meter buffer 324.0 63.01 0
## 2000 meter buffer 324.6 63.55 0
## 1250 meter buffer 335.5 74.48 0
## 1500 meter buffer 338.1 77.14 0
## 1750 meter buffer 341.0 80.04 0
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)For red fox the top model is 3750 m buffer
Let’s get the model summary
summary(fox_mods_no250$`3750 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(red_fox, absent_red_fox) ~ seismic_lines + pipeline + borrowpits +
## wellsites + roads + trails + lc_class20 + lc_class34 + lc_class50 +
## lc_class110 + lc_class210 + lc_class220 + lc_class230 + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 304.3 349.6 -137.1 274.3 137
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 7.606e-10 2.758e-05
## Number of obs: 152, groups: array, 4
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.3604 5.6689 -0.946 0.34437
## seismic_lines 1.0379 2.4927 0.416 0.67715
## pipeline 8.7560 3.4361 2.548 0.01083 *
## borrowpits -44.5280 16.6486 -2.675 0.00748 **
## wellsites -12.9410 6.4374 -2.010 0.04440 *
## roads 1.0242 2.9737 0.344 0.73052
## trails 1.2141 2.8278 0.429 0.66767
## lc_class20 6.1909 6.7804 0.913 0.36122
## lc_class34 28.0557 21.5870 1.300 0.19372
## lc_class50 -0.8399 5.1401 -0.163 0.87020
## lc_class110 5.7942 5.0389 1.150 0.25018
## lc_class210 4.9452 5.7475 0.860 0.38956
## lc_class220 -1.6918 5.3634 -0.315 0.75243
## lc_class230 -0.3727 4.9820 -0.075 0.94037
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Gagh! Borrow pits does not have a reasonable estimate and SE
White-tailed deer
deer_mods_no250 <- final_df %>%
# remove 350 meter buffer width
purrr::discard_at('250 meter buffer') %>%
# use purrr map to make global models for all other buffer sizes
purrr::map(
~.x %>%
# have to include the `` around the white-tailed_deer or R won't recognize it as a variable because of the -
glmmTMB::glmmTMB(cbind(`white-tailed_deer`, `absent_white-tailed_deer`) ~
seismic_lines +
pipeline +
borrowpits +
wellsites +
roads +
trails +
lc_class20 +
lc_class34 +
lc_class50 +
lc_class110 +
lc_class210 +
lc_class220 +
lc_class230 +
(1|array),
data = .,
family = 'binomial'))
# model selection
model.sel(deer_mods_no250)## Model selection table
## cnd((Int)) dsp((Int)) cnd(brr) cnd(lc_c11) cnd(lc_c20)
## 2500 meter buffer 4.449 + -13.9700 -2.9190 -1.7080
## 2750 meter buffer 6.563 + -12.9600 -4.4960 -3.1680
## 4750 meter buffer 2.988 + -11.1400 -0.2994 -6.3250
## 5000 meter buffer 3.271 + -10.1700 -0.2263 -7.7080
## 2250 meter buffer 3.505 + -12.0900 -2.2360 -0.8935
## 4500 meter buffer 1.816 + -11.2500 0.4433 -4.8170
## 4250 meter buffer 2.148 + -15.2500 -0.5879 -3.2000
## 3000 meter buffer 3.776 + -16.1400 -2.6610 -2.0220
## 3750 meter buffer 2.684 + -15.4900 -1.2780 -3.4710
## 4000 meter buffer 1.762 + -18.9700 -0.8749 -2.1220
## 2000 meter buffer 3.126 + -10.4100 -2.0760 -1.5350
## 3250 meter buffer 4.144 + -5.8400 -2.2140 -3.8770
## 3500 meter buffer 5.690 + -6.3760 -3.2390 -5.3640
## 1750 meter buffer 3.550 + -9.0480 -2.4720 -2.0090
## 1500 meter buffer 3.826 + -13.8900 -2.5630 -3.4080
## 750 meter buffer 1.911 + -5.3960 -1.5810 -1.1940
## 1250 meter buffer 4.386 + -8.2400 -2.9200 -3.6650
## 1000 meter buffer 4.165 + -10.6500 -2.7420 -3.0780
## 500 meter buffer 1.976 + -0.3651 -0.7439 -0.8363
## cnd(lc_c21) cnd(lc_c22) cnd(lc_c23) cnd(lc_c34) cnd(lc_c50)
## 2500 meter buffer -0.87940 -1.46300 -4.2760 -59.850 -5.517
## 2750 meter buffer -2.37500 -3.51700 -5.9550 -71.510 -7.653
## 4750 meter buffer 2.00200 -0.06123 -0.9652 -149.000 -5.193
## 5000 meter buffer 1.23400 0.22020 -0.7564 -165.500 -5.356
## 2250 meter buffer 0.09957 0.14690 -3.5810 -43.730 -3.789
## 4500 meter buffer 3.47500 -0.13640 -0.7182 -131.100 -4.043
## 4250 meter buffer 2.39300 -0.33740 -1.7970 -120.000 -4.636
## 3000 meter buffer -0.16560 -0.91040 -4.9410 -66.660 -5.607
## 3750 meter buffer 2.13000 -1.15300 -3.8980 -81.490 -5.012
## 4000 meter buffer 3.29000 -0.66540 -2.9250 -95.770 -4.413
## 2000 meter buffer -0.31490 -0.17840 -3.2490 -32.570 -3.927
## 3250 meter buffer -0.09955 -1.34500 -4.3950 -77.180 -5.775
## 3500 meter buffer -0.93680 -2.97200 -5.2020 -88.770 -7.019
## 1750 meter buffer -1.77900 -1.19900 -2.8100 -25.590 -4.363
## 1500 meter buffer -3.39300 -1.80200 -3.4400 -18.810 -5.015
## 750 meter buffer -0.87950 0.86670 -0.5545 -6.163 -1.193
## 1250 meter buffer -5.14100 -2.82300 -2.9750 -16.550 -4.948
## 1000 meter buffer -4.75600 -2.13700 -2.2190 -12.210 -3.631
## 500 meter buffer -1.13800 0.36320 -0.1688 -4.073 -1.055
## cnd(ppl) cnd(rds) cnd(ssm_lns) cnd(trl) cnd(wll) df logLik
## 2500 meter buffer 0.48160 -1.32300 -1.2990 -2.08000 2.8420 15 -336.320
## 2750 meter buffer -0.58130 -1.78500 -1.6890 -2.29900 4.1070 15 -337.058
## 4750 meter buffer 0.86930 -1.52400 -1.9100 0.03205 -1.9650 15 -338.898
## 5000 meter buffer 0.46840 -1.68600 -2.1540 -0.10530 -2.0670 15 -339.035
## 2250 meter buffer 0.55350 -1.62300 -1.8310 -2.53700 2.1940 15 -340.144
## 4500 meter buffer 1.02900 -0.92010 -1.2660 0.27020 -1.9190 15 -340.568
## 4250 meter buffer 0.54160 -0.42630 -0.7360 0.24770 -0.1690 15 -341.224
## 3000 meter buffer -0.93460 -0.37020 -0.7497 -1.23900 4.4660 15 -341.911
## 3750 meter buffer -0.63730 -0.17760 -0.5014 -0.40380 2.5760 15 -344.834
## 4000 meter buffer 0.02576 0.34870 -0.1156 0.27040 0.5567 15 -345.197
## 2000 meter buffer 0.87530 -1.14000 -1.4120 -1.89000 1.9070 15 -345.887
## 3250 meter buffer -1.22900 -1.11800 -1.1650 -1.32200 2.5980 15 -346.669
## 3500 meter buffer -1.91000 -1.87000 -1.6590 -1.91900 3.7660 15 -347.851
## 1750 meter buffer 0.43570 -0.85500 -1.1890 -1.75800 0.8253 15 -353.649
## 1500 meter buffer 0.60190 -0.01901 -0.7268 -0.57980 -0.9992 15 -360.550
## 750 meter buffer 0.23730 -1.47500 -1.8050 -2.04200 0.1912 15 -367.854
## 1250 meter buffer 0.31870 -0.21480 -0.5859 -0.63250 -1.6450 15 -368.323
## 1000 meter buffer 0.05693 -0.89100 -1.0910 -1.37100 -1.4910 15 -370.040
## 500 meter buffer 0.45850 -2.20500 -2.0600 -1.11900 -0.9517 15 -373.158
## AICc delta weight
## 2500 meter buffer 706.2 0.00 0.600
## 2750 meter buffer 707.6 1.48 0.287
## 4750 meter buffer 711.3 5.16 0.045
## 5000 meter buffer 711.6 5.43 0.040
## 2250 meter buffer 713.8 7.65 0.013
## 4500 meter buffer 714.7 8.50 0.009
## 4250 meter buffer 716.0 9.81 0.004
## 3000 meter buffer 717.4 11.18 0.002
## 3750 meter buffer 723.2 17.03 0.000
## 4000 meter buffer 723.9 17.76 0.000
## 2000 meter buffer 725.3 19.14 0.000
## 3250 meter buffer 726.9 20.70 0.000
## 3500 meter buffer 729.2 23.06 0.000
## 1750 meter buffer 740.8 34.66 0.000
## 1500 meter buffer 754.6 48.46 0.000
## 750 meter buffer 769.2 63.07 0.000
## 1250 meter buffer 770.2 64.01 0.000
## 1000 meter buffer 773.6 67.44 0.000
## 500 meter buffer 779.8 73.68 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## cond(1 | array)For deer the top model was also the 3750 buffer
Let’s get the model summary
summary(deer_mods_no250$`3750 meter buffer`)## Family: binomial ( logit )
## Formula:
## cbind(`white-tailed_deer`, `absent_white-tailed_deer`) ~ seismic_lines +
## pipeline + borrowpits + wellsites + roads + trails + lc_class20 +
## lc_class34 + lc_class50 + lc_class110 + lc_class210 + lc_class220 +
## lc_class230 + (1 | array)
## Data: .
##
## AIC BIC logLik deviance df.resid
## 719.7 765.0 -344.8 689.7 137
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## array (Intercept) 1.021 1.01
## Number of obs: 152, groups: array, 4
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.6845 2.8508 0.942 0.34636
## seismic_lines -0.5014 0.9714 -0.516 0.60574
## pipeline -0.6373 1.6032 -0.398 0.69096
## borrowpits -15.4939 5.8852 -2.633 0.00847 **
## wellsites 2.5761 2.5555 1.008 0.31342
## roads -0.1776 1.2096 -0.147 0.88329
## trails -0.4038 1.1031 -0.366 0.71434
## lc_class20 -3.4711 3.4728 -1.000 0.31755
## lc_class34 -81.4906 15.1035 -5.395 6.83e-08 ***
## lc_class50 -5.0125 2.6098 -1.921 0.05478 .
## lc_class110 -1.2782 2.7120 -0.471 0.63742
## lc_class210 2.1301 2.8871 0.738 0.46064
## lc_class220 -1.1534 2.6142 -0.441 0.65907
## lc_class230 -3.8981 2.6216 -1.487 0.13703
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Coefficient plots
This is also a dog shit way to do this but I need to get this done
coeffs <- confint(black_bear_mods_no250$`500 meter buffer`) %>%
as_tibble() %>%
# subset to just the HFI variables for these plots
slice_head(n = 6) %>%
# add a column where we can put the feature names
rowid_to_column() %>%
# rename the columns
rename('lower' = `2.5 %`,
'upper' = `97.5 %`,
'estimate' = Estimate,
'feature' = rowid) %>%
# rename the entries to features, need to look at the order the features are in from the model summary and ensure it matches
mutate(feature = as.factor(feature),
feature = recode(feature,
'1' = 'seismic_lines',
'2' = 'pipeline',
'3' = 'borrowpits',
'4' = 'wellsites',
'5' = 'roads',
'6' = 'trails'))
# print
coeffs## # A tibble: 6 × 4
## feature lower upper estimate
## <fct> <dbl> <dbl> <dbl>
## 1 seismic_lines -3.52 2.78 -0.370
## 2 pipeline -0.863 0.615 -0.124
## 3 borrowpits -1.53 0.731 -0.401
## 4 wellsites -9.74 4.28 -2.73
## 5 roads -1.12 1.98 0.430
## 6 trails -1.14 0.883 -0.131Try ad do this for all top models at once with purrr
top_models <- list(black_bear_mods_no250$`500 meter buffer`,
caribou_mods_no250$`1250 meter buffer`,
coyote_mods_no250$`4500 meter buffer`,
fisher_mods_no250$`2000 meter buffer`,
wolf_mods_no250$`4500 meter buffer`,
lynx_mods_no250$`1000 meter buffer`,
moose_mods_no250$`3750 meter buffer`,
fox_mods_no250$`3750 meter buffer`,
deer_mods_no250$`3750 meter buffer`) %>%
# use purrr to create coefficient table for all models
purrr::map(
~.x %>%
confint() %>%
as_tibble() %>%
# subset to just the HFI variables for these plots
slice_head(n = 6) %>%
# add a column where we can put the feature names
rowid_to_column() %>%
# rename the columns
rename('lower' = `2.5 %`,
'upper' = `97.5 %`,
'estimate' = Estimate,
'feature' = rowid) %>%
# rename the entries to features, need to look at the order the features are in from the model summary and ensure it matches
mutate(feature = as.factor(feature),
feature = recode(feature,
'1' = 'seismic_lines',
'2' = 'pipeline',
'3' = 'borrowpits',
'4' = 'wellsites',
'5' = 'roads',
'6' = 'trails'))) %>%
purrr::set_names('Black bear',
'Caribou',
'Coyote',
'Fisher',
'Grey wolf',
'Lynx',
'Moose',
'Red fox',
'White-tailed deer')Merge data into one data frame
coeffs_df_all <- list_rbind(top_models,
names_to = 'species') %>%
mutate(species = as.factor(species),
# add phylopic uuid for each species for plotting
# the uuid is extracted using getuuid with the species name as name = ''
uuid = case_when(species == 'Black bear' ~ get_uuid(name = 'Ursus americanus'),
species == 'Caribou' ~ get_uuid(name = 'Rangifer tarandus'),
species == 'Coyote' ~ get_uuid(name = 'Canis latrans'),
species == 'Fisher' ~ '735066c6-2f3e-4f97-acb1-06f55ae075c9',
species == 'Grey wolf' ~ get_uuid(name = 'Canis lupus'),
species == 'Lynx' ~ get_uuid(name = 'Lynx lynx'),
species == 'Moose' ~ '74eab34a-498c-4614-aece-f02361874f79',
species == 'Red fox' ~ '9c977769-bf1e-44d4-82ab-f9f93dce39ca',
species == 'White-taield deer' ~ '56f6fdb2-15d0-43b5-b13f-714f2cb0f5d0')) %>%
# need to remove problematic estimate which is going to skew plot since its so large compared to others
filter(!c(species == 'Red fox' &
feature == 'wellsites'))After plotting the moose image I don’t like it, let’s manually replace it in the data
# I went on the phylopic website and saw there are three images for moose, I like the last one better so we will use it
get_uuid(name = 'Alces alces',
n = 3)## [1] "df2d0ad0-adb0-49d7-afe5-edc6cad21064"
## [2] "1a20a65d-1342-4833-a9dd-1611b9fb383c"
## [3] "74eab34a-498c-4614-aece-f02361874f79"get_uuid(name = 'Odocoileus virginianus',
n = 3)## [1] "4584be20-4514-4673-a3e8-97e2a6a10e57"
## [2] "49a5a5db-047e-4e17-849b-9f96a93f0d2b"
## [3] "56f6fdb2-15d0-43b5-b13f-714f2cb0f5d0"get_uuid(name = 'Pekania pennanti',
n = 2)## [1] "5e13bb38-4c9f-4c7a-9c5c-0597fd33e4ab"
## [2] "735066c6-2f3e-4f97-acb1-06f55ae075c9"get_uuid(name = 'Vulpes',
n = 2)## [1] "a1116e25-7b50-4666-bef5-de18b6e2778c"
## [2] "9c977769-bf1e-44d4-82ab-f9f93dce39ca"# Then I manually copied this uuid and replaces it in the code aboveTry plotting all
ggplot(coeffs_df_all, aes(x = feature,
y = estimate,
group = uuid)) +
geom_errorbar(aes(ymin = lower,
ymax = upper,
color = feature),
width = 0.4,
linewidth = 0.5,
position = position_dodge(width = 1.2)) +
# add points for each estimate for each covariate and use position = position_dodge to shift the points so all the species don't plot on top of one another
geom_phylopic(aes(x = feature,
y = estimate,
uuid = uuid),
position = position_dodge(width = 1.2),
size = 2)## Warning: Removed 6 rows containing missing values.## Warning: `position_dodge()` requires non-overlapping x intervals
ggplot(coeffs, aes(x = feature, y = estimate)) +
geom_point(size = 3, position = position_dodge(width = 0.5)) +
geom_errorbar(aes(ymin = lower, ymax = upper),
width = 0.4,
linewidth = 1,
position = position_dodge(width = 0.5)) +
geom_hline(yintercept = 0, linetype = "dashed")+
scale_color_manual(values = c("#56B4E9", "#009E73"), name = "Spatial Scale")+
theme_classic()+
ggtitle("Moose Response to Anthropogenic Disturbance Features")+
ylab("Coefficient Estimate \n \u00B1 95% CI")+
scale_x_discrete(labels =c("Borrowpits", "Harvest\nAreas", "Industrial\nSites", "Pipelines","Roads", "Seismic\nLines", "Trails", "Transmission\nLines"))+
theme(axis.title.x = element_blank(),
axis.text.x = element_text(size = 12),
axis.title.y = element_text(size = 14),
legend.title = element_text(size = 12),
plot.title = element_text(size = 15, hjust = 0.5))
If all else fails can use plot_model
function